本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
# 使用同步操作處理文檔
Amazon Textract 可以檢測和分析以 JPEG、PNG、PDF 和 TIFF 格式提供的單頁文檔中的文本。操作是同步的，會以接近即時的速度返回結果。如需有關文件的詳細資訊，請參閱 [文本檢測和文檔分析響應對象](how-it-works-document-layout.md)。
本節介紹瞭如何使用 Amazon Textract 同步檢測和分析單頁文檔中的文本。要檢測和分析多頁文檔中的文本，或異步檢測 JPEG 和 PNG 文檔，請參閲[使用異步操作處理文檔](async.md)。
您可以將 Amazon Textract 同步操作用於以下目的：
+ 文本檢測 — 您可以通過使用[DetectDocumentText](API_DetectDocumentText.md)operation. 如需詳細資訊，請參閱 [偵測文字](how-it-works-detecting.md)。
+ 文本分析 — 您可以通過使用[AnalyzeDocument](API_AnalyzeDocument.md)operation. 如需詳細資訊，請參閱 [分析文檔](how-it-works-analyzing.md)。
+ 發票和收據分析 — 您可以使用 AnalyzeFesent 操作確定單頁發票上檢測到的文本或收據之間的財務關係。如需詳細資訊，請參閱「」[分析發票和收款](invoices-receipts.md)
+ 身份證件分析 — 您可以分析美國政府頒發的身份證件，並提取信息以及身份證件上常見類型的信息。如需更多詳細資訊，請參閱。[分析身分文件](how-it-works-identity.md)。
**Topics**
+ [調用 Amazon Textract 同步操作](sync-calling.md)
+ [使用 Amazon Textract 檢測文檔文本](detecting-document-text.md)
+ [使用 Amazon Textract 分析文檔文本](analyzing-document-text.md)
+ [使用 Amazon Textract 分析發票和收據](analyzing-document-expense.md)
+ [使用 Amazon Textract 分析身份文檔](analyzing-document-identity.md)
# 調用 Amazon Textract 同步操作
Amazon Textract 操作處理存儲在本地文件系統中的文檔圖像，或存儲在 Amazon S3 存儲桶中的文檔圖像。指定輸入文檔所在的位置，方法是使用[Document](API_Document.md)輸入參數。文檔圖像可以採用 PNG、JPEG、PDF 格式或 TIFF 格式。同步操作的結果會立即返回，不會存儲以供檢索。
如需完整範例，請參[使用 Amazon Textract 檢測文檔文本](detecting-document-text.md)。
## 要求
下面介紹了請求在 Amazon Textract 中的工作原理。
### 作為圖像字節傳遞的文檔
您可以通過將文檔圖像作為 base64 編碼字節數組傳遞給 Amazon Textract 操作。一個示例是從本地文件系統加載的文檔圖像。如果您使用的是AWS軟件開發工具包調用 Amazon Textract API 操作。
圖像字節在`Bytes`欄位`Document`輸入參數。以下示例顯示了 Amazon Textract 操作的輸入 JSON，該操作將圖像字節傳遞到`Bytes`輸入參數。
```
{
"Document": {
"Bytes": "/9j/4AAQSk....."
}
}
```
**注意**
如果您使用AWS CLI，您無法將圖像字節傳遞給 Amazon Textract 操作。相反，您必須參考存放在 Amazon S3 儲存貯體中的影像。
以下 Java 代碼演示如何從本機檔案系統載入影像，並呼叫 Amazon Textract 操作。
```
String document="input.png";
ByteBuffer imageBytes;
try (InputStream inputStream = new FileInputStream(new File(document))) {
imageBytes = ByteBuffer.wrap(IOUtils.toByteArray(inputStream));
}
AmazonTextract client = AmazonTextractClientBuilder.defaultClient();
DetectDocumentTextRequest request = new DetectDocumentTextRequest()
.withDocument(new Document()
.withBytes(imageBytes));
DetectDocumentTextResult result = client.detectDocumentText(request);
```
### Amazon S3 儲存貯體中的文件
Amazon Textract 可以分析存放在 Amazon S3 儲存貯體的文件影像。您指定儲存貯體和檔案名稱，方法是使用[S3Object](API_S3Object.md)欄位`Document`輸入參數。以下範例示範 Amazon Textract 操作的輸入 JSON，該操作可處理存放在 Amazon S3 儲存貯體中的檔案。
```
{
"Document": {
"S3Object": {
"Bucket": "bucket",
"Name": "input.png"
}
}
}
```
以下範例示範如何使用存放在 Amazon S3 儲存貯體中的影像來呼叫 Amazon Amazon Textract 操作。
```
String document="input.png";
String bucket="bucket";
AmazonTextract client = AmazonTextractClientBuilder.defaultClient();
DetectDocumentTextRequest request = new DetectDocumentTextRequest()
.withDocument(new Document()
.withS3Object(new S3Object()
.withName(document)
.withBucket(bucket)));
DetectDocumentTextResult result = client.detectDocumentText(request);
```
## 回應
下列範例是來自呼叫的 JSON 回應`DetectDocumentText`。如需詳細資訊，請參閱 [偵測文字](how-it-works-detecting.md)。
```
{
{
"DocumentMetadata": {
"Pages": 1
},
"Blocks": [
{
"BlockType": "PAGE",
"Geometry": {
"BoundingBox": {
"Width": 0.9995205998420715,
"Height": 1.0,
"Left": 0.0,
"Top": 0.0
},
"Polygon": [
{
"X": 0.0,
"Y": 0.0
},
{
"X": 0.9995205998420715,
"Y": 2.297314024515845E-16
},
{
"X": 0.9995205998420715,
"Y": 1.0
},
{
"X": 0.0,
"Y": 1.0
}
]
},
"Id": "ca4b9171-7109-4adb-a811-e09bbe4834dd",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
"26085884-d005-4144-b4c2-4d83dc50739b",
"ee9d01bc-d91c-401d-8c0a-eec76f5f7862",
"404bb3d3-d7ab-4008-a195-5dec87a08664",
"8ae1b4ba-67c1-4486-bd20-54f461886ce9",
"47aab5ab-be2c-4c73-97c7-d0a45454e843",
"dd06bb49-6a56-4ea7-beec-a2aa09835c3c",
"8837153d-81b8-4031-a49f-83a3d81803c2",
"5dae3b74-9e95-4b62-99b7-93b88fe70648",
"4508da80-64d8-42a8-8846-cfafe6eab10c",
"e87be7a9-5519-42e1-b18e-ae10e2d3ed13",
"f04bb223-d075-41c3-b328-7354611c826b",
"a234f0e8-67de-46f4-a7c7-0bbe8d5159ce",
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"445f4fdd-c77b-4a7b-a2fc-6ca07cfe9ed7",
"359f3870-7183-43f5-b638-970f5cefe4d5",
"b9deea0a-244c-4d54-b774-cf03fbaaa8b1",
"e2a43881-f620-44f2-b067-500ce7dc8d4d",
"41756974-64ef-432d-b4b2-34702505975a",
"93d96d32-8b4a-4a98-9578-8b4df4f227a6",
"bc907357-63d6-43c0-ab87-80d7e76d377e",
"2d727ca7-3acb-4bb9-a564-5885c90e9325",
"f32a5989-cbfb-41e6-b0fc-ce1c77c014bd",
"e0ba06d0-dbb6-4962-8047-8cac3adfe45a",
"b6ed204d-ae01-4b75-bb91-c85d4147a37e",
"ac4b9ee0-c9b2-4239-a741-5753e5282033",
"ebc18885-48d7-45b8-90e3-d172b4357802",
"babf6360-789e-49c1-9c78-0784acc14a0c"
]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.93761444091797,
"Text": "Employment Application",
"Geometry": {
"BoundingBox": {
"Width": 0.3391372561454773,
"Height": 0.06906412541866302,
"Left": 0.29548385739326477,
"Top": 0.027493247762322426
},
"Polygon": [
{
"X": 0.29548385739326477,
"Y": 0.027493247762322426
},
{
"X": 0.6346210837364197,
"Y": 0.027493247762322426
},
{
"X": 0.6346210837364197,
"Y": 0.0965573713183403
},
{
"X": 0.29548385739326477,
"Y": 0.0965573713183403
}
]
},
"Id": "26085884-d005-4144-b4c2-4d83dc50739b",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
"ed48dacc-d089-498f-8e93-1cee1e5f39f3",
"ac7370f3-cbb7-4cd9-a8f9-bdcb2252caaf"
]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.91246795654297,
"Text": "Application Information",
"Geometry": {
"BoundingBox": {
"Width": 0.19878505170345306,
"Height": 0.03754019737243652,
"Left": 0.03988289833068848,
"Top": 0.14050349593162537
},
"Polygon": [
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},
{
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},
{
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},
{
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"Y": 0.1780436933040619
}
]
},
"Id": "ee9d01bc-d91c-401d-8c0a-eec76f5f7862",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
"efe3fc6d-becb-4520-80ee-49a329386aee",
"c2260852-6cfd-4a71-9fc6-62b2f9b02355"
]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.88693237304688,
"Text": "Full Name: Jane Doe",
"Geometry": {
"BoundingBox": {
"Width": 0.16733919084072113,
"Height": 0.031106337904930115,
"Left": 0.03899926319718361,
"Top": 0.21361036598682404
},
"Polygon": [
{
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"Y": 0.21361036598682404
},
{
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},
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"Y": 0.24471670389175415
},
{
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}
]
},
"Id": "404bb3d3-d7ab-4008-a195-5dec87a08664",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.9206314086914,
"Text": "Phone Number: 555-0100",
"Geometry": {
"BoundingBox": {
"Width": 0.3115004599094391,
"Height": 0.047169625759124756,
"Left": 0.03604753687977791,
"Top": 0.2812676727771759
},
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},
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},
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},
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}
]
},
"Id": "8ae1b4ba-67c1-4486-bd20-54f461886ce9",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.48902893066406,
"Text": "Home Address: 123 Any Street, Any Town. USA",
"Geometry": {
"BoundingBox": {
"Width": 0.7431139945983887,
"Height": 0.09577702730894089,
"Left": 0.03359385207295418,
"Top": 0.3258342146873474
},
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},
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}
]
},
"Id": "47aab5ab-be2c-4c73-97c7-d0a45454e843",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.89382934570312,
"Text": "Mailing Address: same as above",
"Geometry": {
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},
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}
]
},
"Id": "dd06bb49-6a56-4ea7-beec-a2aa09835c3c",
"Relationships": [
{
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"Ids": [
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 94.67343139648438,
"Text": "Previous Employment History",
"Geometry": {
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"Height": 0.051920413970947266,
"Left": 0.3194798231124878,
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}
]
},
"Id": "8837153d-81b8-4031-a49f-83a3d81803c2",
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{
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"Ids": [
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.66949462890625,
"Text": "Start Date",
"Geometry": {
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"Height": 0.030944595113396645,
"Left": 0.034429505467414856,
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},
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},
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}
]
},
"Id": "5dae3b74-9e95-4b62-99b7-93b88fe70648",
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{
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.86717224121094,
"Text": "End Date",
"Geometry": {
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]
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]
},
{
"BlockType": "LINE",
"Confidence": 99.9539794921875,
"Text": "Employer Name",
"Geometry": {
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"Height": 0.0392492413520813,
"Left": 0.2647075653076172,
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},
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}
]
},
"Id": "e87be7a9-5519-42e1-b18e-ae10e2d3ed13",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
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"9f0f9c06-d02c-4b07-bb39-7ade70be2c1b"
]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.35584259033203,
"Text": "Position Held",
"Geometry": {
"BoundingBox": {
"Width": 0.11393272876739502,
"Height": 0.03415105864405632,
"Left": 0.49973347783088684,
"Top": 0.614840030670166
},
"Polygon": [
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},
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},
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}
]
},
"Id": "f04bb223-d075-41c3-b328-7354611c826b",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
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"3297ab59-b237-45fb-ae60-a108f0c95ac2"
]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.9817886352539,
"Text": "Reason for leaving",
"Geometry": {
"BoundingBox": {
"Width": 0.16511960327625275,
"Height": 0.04062700271606445,
"Left": 0.7430596351623535,
"Top": 0.6116235852241516
},
"Polygon": [
{
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},
{
"X": 0.9081792235374451,
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},
{
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"Y": 0.6522505879402161
},
{
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}
]
},
"Id": "a234f0e8-67de-46f4-a7c7-0bbe8d5159ce",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
"f4b8cf26-d2da-4a76-8345-69562de3cc11",
"386d4a63-1194-4c0e-a18d-4d074a0b1f93",
"a8622541-1896-4d54-8d10-7da2c800ec5c"
]
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```
# 使用 Amazon Textract 檢測文檔文本
若要檢測文檔中的文本，請使用[DetectDocumentText](API_DetectDocumentText.md)操作，並將文檔文件作為輸入傳遞。`DetectDocumentText`返回一個 JSON 結構，其中包含檢測到的文本的行和單詞、文本在文檔中的位置以及檢測到的文本之間的關係。如需詳細資訊，請參閱 [偵測文字](how-it-works-detecting.md)。
您提供的輸入文檔可以是影像位元組陣列 (base64 編碼影像位元組)，或者 Amazon S3 物件。在此步驟中，您會將影像檔案上傳至您的 S3 儲存貯體，並指定檔案名稱。
**若要檢測文檔中的文字 (API)**
1. 如果您尚未：
1. 使用創建或更新 IAM 用户`AmazonTextractFullAccess`和`AmazonS3ReadOnlyAccess`許可。如需詳細資訊，請參閱 [步驟 1：設定 AWS 帳户並建立 IAM 使用者](setting-up.md#setting-up-iam)。
1. 安裝並設定 AWS CLI 和 AWS SDK。如需詳細資訊，請參閱 [步驟 2：設定AWS CLI和AWS開發套件](setup-awscli-sdk.md)。
1. 將檔案上傳至 S3 儲存貯體。
如需說明，請參閱「」[將物件上傳至 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)中的*Amazon Simple Storage Service 用户指南*。
1. 使用下列範例來呼叫 `DetectDocumentText` 操作。
------
#### [ Java ]
以下示例代碼在檢測到的文本行周圍顯示文檔和框。
在函數中`main`中，替換`bucket`和`document`以您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與文件名稱來取代和。
```
//Calls DetectDocumentText.
//Loads document from S3 bucket. Displays the document and bounding boxes around detected lines/words of text.
package com.amazonaws.samples;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.List;
import javax.imageio.ImageIO;
import javax.swing.*;
import com.amazonaws.services.s3.AmazonS3;
import com.amazonaws.services.s3.AmazonS3ClientBuilder;
import com.amazonaws.services.s3.model.S3ObjectInputStream;
import com.amazonaws.client.builder.AwsClientBuilder.EndpointConfiguration;
import com.amazonaws.services.textract.AmazonTextract;
import com.amazonaws.services.textract.AmazonTextractClientBuilder;
import com.amazonaws.services.textract.model.Block;
import com.amazonaws.services.textract.model.BoundingBox;
import com.amazonaws.services.textract.model.DetectDocumentTextRequest;
import com.amazonaws.services.textract.model.DetectDocumentTextResult;
import com.amazonaws.services.textract.model.Document;
import com.amazonaws.services.textract.model.S3Object;
import com.amazonaws.services.textract.model.Point;
import com.amazonaws.services.textract.model.Relationship;
public class DocumentText extends JPanel {
private static final long serialVersionUID = 1L;
BufferedImage image;
DetectDocumentTextResult result;
public DocumentText(DetectDocumentTextResult documentResult, BufferedImage bufImage) throws Exception {
super();
result = documentResult; // Results of text detection.
image = bufImage; // The image containing the document.
}
// Draws the image and text bounding box.
public void paintComponent(Graphics g) {
int height = image.getHeight(this);
int width = image.getWidth(this);
Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g.
// Draw the image.
g2d.drawImage(image, 0, 0, image.getWidth(this) , image.getHeight(this), this);
// Iterate through blocks and display polygons around lines of detected text.
List blocks = result.getBlocks();
for (Block block : blocks) {
DisplayBlockInfo(block);
if ((block.getBlockType()).equals("LINE")) {
ShowPolygon(height, width, block.getGeometry().getPolygon(), g2d);
/*
ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d);
*/
} else { // its a word, so just show vertical lines.
ShowPolygonVerticals(height, width, block.getGeometry().getPolygon(), g2d);
}
}
}
// Show bounding box at supplied location.
private void ShowBoundingBox(int imageHeight, int imageWidth, BoundingBox box, Graphics2D g2d) {
float left = imageWidth * box.getLeft();
float top = imageHeight * box.getTop();
// Display bounding box.
g2d.setColor(new Color(0, 212, 0));
g2d.drawRect(Math.round(left), Math.round(top),
Math.round(imageWidth * box.getWidth()), Math.round(imageHeight * box.getHeight()));
}
// Shows polygon at supplied location
private void ShowPolygon(int imageHeight, int imageWidth, List points, Graphics2D g2d) {
g2d.setColor(new Color(0, 0, 0));
Polygon polygon = new Polygon();
// Construct polygon and display
for (Point point : points) {
polygon.addPoint((Math.round(point.getX() * imageWidth)),
Math.round(point.getY() * imageHeight));
}
g2d.drawPolygon(polygon);
}
// Draws only the vertical lines in the supplied polygon.
private void ShowPolygonVerticals(int imageHeight, int imageWidth, List points, Graphics2D g2d) {
g2d.setColor(new Color(0, 212, 0));
Object[] parry = points.toArray();
g2d.setStroke(new BasicStroke(2));
g2d.drawLine(Math.round(((Point) parry[0]).getX() * imageWidth),
Math.round(((Point) parry[0]).getY() * imageHeight), Math.round(((Point) parry[3]).getX() * imageWidth),
Math.round(((Point) parry[3]).getY() * imageHeight));
g2d.setColor(new Color(255, 0, 0));
g2d.drawLine(Math.round(((Point) parry[1]).getX() * imageWidth),
Math.round(((Point) parry[1]).getY() * imageHeight), Math.round(((Point) parry[2]).getX() * imageWidth),
Math.round(((Point) parry[2]).getY() * imageHeight));
}
//Displays information from a block returned by text detection and text analysis
private void DisplayBlockInfo(Block block) {
System.out.println("Block Id : " + block.getId());
if (block.getText()!=null)
System.out.println(" Detected text: " + block.getText());
System.out.println(" Type: " + block.getBlockType());
if (block.getBlockType().equals("PAGE") !=true) {
System.out.println(" Confidence: " + block.getConfidence().toString());
}
if(block.getBlockType().equals("CELL"))
{
System.out.println(" Cell information:");
System.out.println(" Column: " + block.getColumnIndex());
System.out.println(" Row: " + block.getRowIndex());
System.out.println(" Column span: " + block.getColumnSpan());
System.out.println(" Row span: " + block.getRowSpan());
}
System.out.println(" Relationships");
List relationships=block.getRelationships();
if(relationships!=null) {
for (Relationship relationship : relationships) {
System.out.println(" Type: " + relationship.getType());
System.out.println(" IDs: " + relationship.getIds().toString());
}
} else {
System.out.println(" No related Blocks");
}
System.out.println(" Geometry");
System.out.println(" Bounding Box: " + block.getGeometry().getBoundingBox().toString());
System.out.println(" Polygon: " + block.getGeometry().getPolygon().toString());
List entityTypes = block.getEntityTypes();
System.out.println(" Entity Types");
if(entityTypes!=null) {
for (String entityType : entityTypes) {
System.out.println(" Entity Type: " + entityType);
}
} else {
System.out.println(" No entity type");
}
if(block.getPage()!=null)
System.out.println(" Page: " + block.getPage());
System.out.println();
}
public static void main(String arg[]) throws Exception {
// The S3 bucket and document
String document = "";
String bucket = "";
AmazonS3 s3client = AmazonS3ClientBuilder.standard()
.withEndpointConfiguration(
new EndpointConfiguration("https://s3.amazonaws.com","us-east-1"))
.build();
// Get the document from S3
com.amazonaws.services.s3.model.S3Object s3object = s3client.getObject(bucket, document);
S3ObjectInputStream inputStream = s3object.getObjectContent();
BufferedImage image = ImageIO.read(inputStream);
// Call DetectDocumentText
EndpointConfiguration endpoint = new EndpointConfiguration(
"https://textract.us-east-1.amazonaws.com", "us-east-1");
AmazonTextract client = AmazonTextractClientBuilder.standard()
.withEndpointConfiguration(endpoint).build();
DetectDocumentTextRequest request = new DetectDocumentTextRequest()
.withDocument(new Document().withS3Object(new S3Object().withName(document).withBucket(bucket)));
DetectDocumentTextResult result = client.detectDocumentText(request);
// Create frame and panel.
JFrame frame = new JFrame("RotateImage");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
DocumentText panel = new DocumentText(result, image);
panel.setPreferredSize(new Dimension(image.getWidth() , image.getHeight() ));
frame.setContentPane(panel);
frame.pack();
frame.setVisible(true);
}
}
```
------
#### [ AWS CLI ]
此 AWS CLI 命令顯示 `detect-document-text` CLI 操作的 JSON 輸出。
替換`Bucket`和`Name`以您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與文件名稱來取代和。
```
aws textract detect-document-text \
--document '{"S3Object":{"Bucket":"bucket","Name":"document"}}'
```
------
#### [ Python ]
以下範例程式碼會顯示在偵測到的文本行周圍的文件和框。
在函數中`main`中，替換`bucket`和`document`以您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與文件名稱來取代和。
```
#Detects text in a document stored in an S3 bucket. Display polygon box around text and angled text
import boto3
import io
from io import BytesIO
import sys
import psutil
import time
import math
from PIL import Image, ImageDraw, ImageFont
# Displays information about a block returned by text detection and text analysis
def DisplayBlockInformation(block):
print('Id: {}'.format(block['Id']))
if 'Text' in block:
print(' Detected: ' + block['Text'])
print(' Type: ' + block['BlockType'])
if 'Confidence' in block:
print(' Confidence: ' + "{:.2f}".format(block['Confidence']) + "%")
if block['BlockType'] == 'CELL':
print(" Cell information")
print(" Column: " + str(block['ColumnIndex']))
print(" Row: " + str(block['RowIndex']))
print(" ColumnSpan: " + str(block['ColumnSpan']))
print(" RowSpan: " + str(block['RowSpan']))
if 'Relationships' in block:
print(' Relationships: {}'.format(block['Relationships']))
print(' Geometry: ')
print(' Bounding Box: {}'.format(block['Geometry']['BoundingBox']))
print(' Polygon: {}'.format(block['Geometry']['Polygon']))
if block['BlockType'] == "KEY_VALUE_SET":
print (' Entity Type: ' + block['EntityTypes'][0])
if 'Page' in block:
print('Page: ' + block['Page'])
print()
def process_text_detection(bucket, document):
#Get the document from S3
s3_connection = boto3.resource('s3')
s3_object = s3_connection.Object(bucket,document)
s3_response = s3_object.get()
stream = io.BytesIO(s3_response['Body'].read())
image=Image.open(stream)
# Detect text in the document
client = boto3.client('textract')
#process using image bytes
#image_binary = stream.getvalue()
#response = client.detect_document_text(Document={'Bytes': image_binary})
#process using S3 object
response = client.detect_document_text(
Document={'S3Object': {'Bucket': bucket, 'Name': document}})
#Get the text blocks
blocks=response['Blocks']
width, height =image.size
draw = ImageDraw.Draw(image)
print ('Detected Document Text')
# Create image showing bounding box/polygon the detected lines/text
for block in blocks:
print('Type: ' + block['BlockType'])
if block['BlockType'] != 'PAGE':
print('Detected: ' + block['Text'])
print('Confidence: ' + "{:.2f}".format(block['Confidence']) + "%")
print('Id: {}'.format(block['Id']))
if 'Relationships' in block:
print('Relationships: {}'.format(block['Relationships']))
print('Bounding Box: {}'.format(block['Geometry']['BoundingBox']))
print('Polygon: {}'.format(block['Geometry']['Polygon']))
print()
draw=ImageDraw.Draw(image)
# Draw WORD - Green - start of word, red - end of word
if block['BlockType'] == "WORD":
draw.line([(width * block['Geometry']['Polygon'][0]['X'],
height * block['Geometry']['Polygon'][0]['Y']),
(width * block['Geometry']['Polygon'][3]['X'],
height * block['Geometry']['Polygon'][3]['Y'])],fill='green',
width=2)
draw.line([(width * block['Geometry']['Polygon'][1]['X'],
height * block['Geometry']['Polygon'][1]['Y']),
(width * block['Geometry']['Polygon'][2]['X'],
height * block['Geometry']['Polygon'][2]['Y'])],
fill='red',
width=2)
# Draw box around entire LINE
if block['BlockType'] == "LINE":
points=[]
for polygon in block['Geometry']['Polygon']:
points.append((width * polygon['X'], height * polygon['Y']))
draw.polygon((points), outline='black')
# Uncomment to draw bounding box
#box=block['Geometry']['BoundingBox']
#left = width * box['Left']
#top = height * box['Top']
#draw.rectangle([left,top, left + (width * box['Width']), top +(height * box['Height'])],outline='black')
# Display the image
image.show()
# display image for 10 seconds
return len(blocks)
def main():
bucket = ''
document = ''
block_count=process_text_detection(bucket,document)
print("Blocks detected: " + str(block_count))
if __name__ == "__main__":
main()
```
------
#### [ Node.js ]
以下 Node.js 示例代碼顯示檢測到的文本行周圍的文檔和框，從而將結果的圖像輸出到運行代碼的目錄。它利用`image-size`和`images`套件。
在函數中`main`中，替換`bucket`和`document`以您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與文件名稱來取代和。替換`regionConfig`以及您賬户所在區域的名稱。
```
async function main(){
// Import AWS
const AWS = require("aws-sdk")
// Use Image-Size to get
const sizeOf = require('image-size');
// Image tool to draw buffers
const images = require("images");
// Create a canvas and get the context
const { createCanvas } = require('canvas')
const canvas = createCanvas(200, 200)
const ctx = canvas.getContext('2d')
// Set variables
const bucket = 'bucket-name' // the s3 bucket name
const photo = 'image-name' // the name of file
const regionConfig = 'region'
// Set region if needed
AWS.config.update({region:regionConfig});
// Connect to Textract
const client = new AWS.Textract();
// Connect to S3 to display image
const s3 = new AWS.S3();
// Define paramaters
const params = {
Document: {
S3Object: {
Bucket: bucket,
Name: photo
},
},
}
// Function to display image
async function getImage(){
const imageData = s3.getObject(
{
Bucket: bucket,
Key: photo
}
).promise();
return imageData;
}
// get image
var imageData = await getImage()
// Get the height, width of the image
const dimensions = sizeOf(imageData.Body)
const width = dimensions.width
const height = dimensions.height
console.log(imageData.Body)
console.log(width, height)
canvas.width = width;
canvas.height = height;
try{
// Call API and log response
const res = await client.detectDocumentText(params).promise();
var image = images(imageData.Body).size(width, height)
//console.log the type of block, text, text type, and confidence
res.Blocks.forEach(block => {
console.log(`Block Type: ${block.BlockType}`),
console.log(`Text: ${block.Text}`)
console.log(`TextType: ${block.TextType}`)
console.log(`Confidence: ${block.Confidence}`)
// Draw box around detected text using polygons
ctx.strokeStyle = 'rgba(0,0,0,0.5)';
ctx.beginPath();
block.Geometry.Polygon.forEach(({X, Y}) =>
ctx.lineTo(width * X - 10, height * Y - 10)
);
ctx.closePath();
ctx.stroke();
console.log("-----")
})
// render image
var buffer = canvas.toBuffer("image/png");
image.draw(images(buffer), 10, 10)
image.save("output-image.jpg");
} catch (err){
console.error(err);}
}
main()
```
------
1. 執行範例。Python 和 Java 示例顯示了文檔圖像。每一行檢測到的文本周圍都有一個黑框。綠色的垂直線是檢測到的單詞的開頭。紅色垂直線是檢測到的單詞的結尾。所以此AWS CLI示例僅顯示`DetectDocumentText`operation.
# 使用 Amazon Textract 分析文檔文本
若要分析文檔中的文本，請使用[AnalyzeDocument](API_AnalyzeDocument.md)操作，並將文檔文件作為輸入傳遞。`AnalyzeDocument`會傳回含有分析文字的 JSON 結構。如需詳細資訊，請參閱 [分析文檔](how-it-works-analyzing.md)。
您提供的輸入文檔可以是影像位元組陣列 (base64 編碼影像位元組)，或者 Amazon S3 物件。在此步驟中，您會將影像檔案上傳至您的 S3 儲存貯體，並指定檔案名稱。
**分析文檔中的文本 (API)**
1. 如果您尚未：
1. 使用創建或更新 IAM 用户`AmazonTextractFullAccess`和`AmazonS3ReadOnlyAccess`許可。如需詳細資訊，請參閱 [步驟 1：設定 AWS 帳户並建立 IAM 使用者](setting-up.md#setting-up-iam)。
1. 安裝並設定 AWS CLI 和 AWS SDK。如需詳細資訊，請參閱 [步驟 2：設定AWS CLI和AWS開發套件](setup-awscli-sdk.md)。
1. 將含有文檔的影像上傳至您的 S3 儲存貯體。
如需說明，請參閱「」[將物件上傳至 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)中的*Amazon Simple Storage Service 用户指南*。
1. 使用下列範例來呼叫 `AnalyzeDocument` 操作。
------
#### [ Java ]
以下範例程式碼會顯示檢測到的項目周圍的文件和框。
在函數中`main`中，替換`bucket`和`document`以您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與文件影像名稱來取代和。
```
//Loads document from S3 bucket. Displays the document and polygon around detected lines of text.
package com.amazonaws.samples;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.List;
import javax.imageio.ImageIO;
import javax.swing.*;
import com.amazonaws.services.s3.AmazonS3;
import com.amazonaws.services.s3.AmazonS3ClientBuilder;
import com.amazonaws.services.s3.model.S3ObjectInputStream;
import com.amazonaws.services.textract.AmazonTextract;
import com.amazonaws.services.textract.AmazonTextractClientBuilder;
import com.amazonaws.services.textract.model.AnalyzeDocumentRequest;
import com.amazonaws.services.textract.model.AnalyzeDocumentResult;
import com.amazonaws.services.textract.model.Block;
import com.amazonaws.services.textract.model.BoundingBox;
import com.amazonaws.services.textract.model.Document;
import com.amazonaws.services.textract.model.S3Object;
import com.amazonaws.services.textract.model.Point;
import com.amazonaws.services.textract.model.Relationship;
import com.amazonaws.client.builder.AwsClientBuilder.EndpointConfiguration;
public class AnalyzeDocument extends JPanel {
private static final long serialVersionUID = 1L;
BufferedImage image;
AnalyzeDocumentResult result;
public AnalyzeDocument(AnalyzeDocumentResult documentResult, BufferedImage bufImage) throws Exception {
super();
result = documentResult; // Results of text detection.
image = bufImage; // The image containing the document.
}
// Draws the image and text bounding box.
public void paintComponent(Graphics g) {
int height = image.getHeight(this);
int width = image.getWidth(this);
Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g.
// Draw the image.
g2d.drawImage(image, 0, 0, image.getWidth(this), image.getHeight(this), this);
// Iterate through blocks and display bounding boxes around everything.
List blocks = result.getBlocks();
for (Block block : blocks) {
DisplayBlockInfo(block);
switch(block.getBlockType()) {
case "KEY_VALUE_SET":
if (block.getEntityTypes().contains("KEY")){
ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(255,0,0));
}
else { //VALUE
ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(0,255,0));
}
break;
case "TABLE":
ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(0,0,255));
break;
case "CELL":
ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(255,255,0));
break;
case "SELECTION_ELEMENT":
if (block.getSelectionStatus().equals("SELECTED"))
ShowSelectedElement(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(0,0,255));
break;
default:
//PAGE, LINE & WORD
//ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(200,200,0));
}
}
// uncomment to show polygon around all blocks
//ShowPolygon(height,width,block.getGeometry().getPolygon(),g2d);
}
// Show bounding box at supplied location.
private void ShowBoundingBox(int imageHeight, int imageWidth, BoundingBox box, Graphics2D g2d, Color color) {
float left = imageWidth * box.getLeft();
float top = imageHeight * box.getTop();
// Display bounding box.
g2d.setColor(color);
g2d.drawRect(Math.round(left), Math.round(top),
Math.round(imageWidth * box.getWidth()), Math.round(imageHeight * box.getHeight()));
}
private void ShowSelectedElement(int imageHeight, int imageWidth, BoundingBox box, Graphics2D g2d, Color color) {
float left = imageWidth * box.getLeft();
float top = imageHeight * box.getTop();
// Display bounding box.
g2d.setColor(color);
g2d.fillRect(Math.round(left), Math.round(top),
Math.round(imageWidth * box.getWidth()), Math.round(imageHeight * box.getHeight()));
}
// Shows polygon at supplied location
private void ShowPolygon(int imageHeight, int imageWidth, List points, Graphics2D g2d) {
g2d.setColor(new Color(0, 0, 0));
Polygon polygon = new Polygon();
// Construct polygon and display
for (Point point : points) {
polygon.addPoint((Math.round(point.getX() * imageWidth)),
Math.round(point.getY() * imageHeight));
}
g2d.drawPolygon(polygon);
}
//Displays information from a block returned by text detection and text analysis
private void DisplayBlockInfo(Block block) {
System.out.println("Block Id : " + block.getId());
if (block.getText()!=null)
System.out.println(" Detected text: " + block.getText());
System.out.println(" Type: " + block.getBlockType());
if (block.getBlockType().equals("PAGE") !=true) {
System.out.println(" Confidence: " + block.getConfidence().toString());
}
if(block.getBlockType().equals("CELL"))
{
System.out.println(" Cell information:");
System.out.println(" Column: " + block.getColumnIndex());
System.out.println(" Row: " + block.getRowIndex());
System.out.println(" Column span: " + block.getColumnSpan());
System.out.println(" Row span: " + block.getRowSpan());
}
System.out.println(" Relationships");
List relationships=block.getRelationships();
if(relationships!=null) {
for (Relationship relationship : relationships) {
System.out.println(" Type: " + relationship.getType());
System.out.println(" IDs: " + relationship.getIds().toString());
}
} else {
System.out.println(" No related Blocks");
}
System.out.println(" Geometry");
System.out.println(" Bounding Box: " + block.getGeometry().getBoundingBox().toString());
System.out.println(" Polygon: " + block.getGeometry().getPolygon().toString());
List entityTypes = block.getEntityTypes();
System.out.println(" Entity Types");
if(entityTypes!=null) {
for (String entityType : entityTypes) {
System.out.println(" Entity Type: " + entityType);
}
} else {
System.out.println(" No entity type");
}
if(block.getBlockType().equals("SELECTION_ELEMENT")) {
System.out.print(" Selection element detected: ");
if (block.getSelectionStatus().equals("SELECTED")){
System.out.println("Selected");
}else {
System.out.println(" Not selected");
}
}
if(block.getPage()!=null)
System.out.println(" Page: " + block.getPage());
System.out.println();
}
public static void main(String arg[]) throws Exception {
// The S3 bucket and document
String document = "";
String bucket = "";
AmazonS3 s3client = AmazonS3ClientBuilder.standard()
.withEndpointConfiguration(
new EndpointConfiguration("https://s3.amazonaws.com","us-east-1"))
.build();
// Get the document from S3
com.amazonaws.services.s3.model.S3Object s3object = s3client.getObject(bucket, document);
S3ObjectInputStream inputStream = s3object.getObjectContent();
BufferedImage image = ImageIO.read(inputStream);
// Call AnalyzeDocument
EndpointConfiguration endpoint = new EndpointConfiguration(
"https://textract.us-east-1.amazonaws.com", "us-east-1");
AmazonTextract client = AmazonTextractClientBuilder.standard()
.withEndpointConfiguration(endpoint).build();
AnalyzeDocumentRequest request = new AnalyzeDocumentRequest()
.withFeatureTypes("TABLES","FORMS")
.withDocument(new Document().
withS3Object(new S3Object().withName(document).withBucket(bucket)));
AnalyzeDocumentResult result = client.analyzeDocument(request);
// Create frame and panel.
JFrame frame = new JFrame("RotateImage");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
AnalyzeDocument panel = new AnalyzeDocument(result, image);
panel.setPreferredSize(new Dimension(image.getWidth(), image.getHeight()));
frame.setContentPane(panel);
frame.pack();
frame.setVisible(true);
}
}
```
------
#### [ AWS CLI ]
此 AWS CLI 命令顯示 `detect-document-text` CLI 操作的 JSON 輸出。
替換`Bucket`和`Name`以您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與文件名稱來取代和。
```
aws textract analyze-document \
--document '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \
--feature-types '["TABLES","FORMS"]'
```
------
#### [ Python ]
以下範例程式碼會顯示檢測到的項目周圍的文件和框。
在函數中`main`中，替換`bucket`和`document`以您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與文件名稱來取代和。
```
#Analyzes text in a document stored in an S3 bucket. Display polygon box around text and angled text
import boto3
import io
from io import BytesIO
import sys
import math
from PIL import Image, ImageDraw, ImageFont
def ShowBoundingBox(draw,box,width,height,boxColor):
left = width * box['Left']
top = height * box['Top']
draw.rectangle([left,top, left + (width * box['Width']), top +(height * box['Height'])],outline=boxColor)
def ShowSelectedElement(draw,box,width,height,boxColor):
left = width * box['Left']
top = height * box['Top']
draw.rectangle([left,top, left + (width * box['Width']), top +(height * box['Height'])],fill=boxColor)
# Displays information about a block returned by text detection and text analysis
def DisplayBlockInformation(block):
print('Id: {}'.format(block['Id']))
if 'Text' in block:
print(' Detected: ' + block['Text'])
print(' Type: ' + block['BlockType'])
if 'Confidence' in block:
print(' Confidence: ' + "{:.2f}".format(block['Confidence']) + "%")
if block['BlockType'] == 'CELL':
print(" Cell information")
print(" Column:" + str(block['ColumnIndex']))
print(" Row:" + str(block['RowIndex']))
print(" Column Span:" + str(block['ColumnSpan']))
print(" RowSpan:" + str(block['ColumnSpan']))
if 'Relationships' in block:
print(' Relationships: {}'.format(block['Relationships']))
print(' Geometry: ')
print(' Bounding Box: {}'.format(block['Geometry']['BoundingBox']))
print(' Polygon: {}'.format(block['Geometry']['Polygon']))
if block['BlockType'] == "KEY_VALUE_SET":
print (' Entity Type: ' + block['EntityTypes'][0])
if block['BlockType'] == 'SELECTION_ELEMENT':
print(' Selection element detected: ', end='')
if block['SelectionStatus'] =='SELECTED':
print('Selected')
else:
print('Not selected')
if 'Page' in block:
print('Page: ' + block['Page'])
print()
def process_text_analysis(bucket, document):
#Get the document from S3
s3_connection = boto3.resource('s3')
s3_object = s3_connection.Object(bucket,document)
s3_response = s3_object.get()
stream = io.BytesIO(s3_response['Body'].read())
image=Image.open(stream)
# Analyze the document
client = boto3.client('textract')
image_binary = stream.getvalue()
response = client.analyze_document(Document={'Bytes': image_binary},
FeatureTypes=["TABLES", "FORMS"])
### Alternatively, process using S3 object ###
#response = client.analyze_document(
# Document={'S3Object': {'Bucket': bucket, 'Name': document}},
# FeatureTypes=["TABLES", "FORMS"])
### To use a local file ###
# with open("pathToFile", 'rb') as img_file:
### To display image using PIL ###
# image = Image.open()
### Read bytes ###
# img_bytes = img_file.read()
# response = client.analyze_document(Document={'Bytes': img_bytes}, FeatureTypes=["TABLES", "FORMS"])
#Get the text blocks
blocks=response['Blocks']
width, height =image.size
draw = ImageDraw.Draw(image)
print ('Detected Document Text')
# Create image showing bounding box/polygon the detected lines/text
for block in blocks:
DisplayBlockInformation(block)
draw=ImageDraw.Draw(image)
if block['BlockType'] == "KEY_VALUE_SET":
if block['EntityTypes'][0] == "KEY":
ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height,'red')
else:
ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height,'green')
if block['BlockType'] == 'TABLE':
ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height, 'blue')
if block['BlockType'] == 'CELL':
ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height, 'yellow')
if block['BlockType'] == 'SELECTION_ELEMENT':
if block['SelectionStatus'] =='SELECTED':
ShowSelectedElement(draw, block['Geometry']['BoundingBox'],width,height, 'blue')
#uncomment to draw polygon for all Blocks
#points=[]
#for polygon in block['Geometry']['Polygon']:
# points.append((width * polygon['X'], height * polygon['Y']))
#draw.polygon((points), outline='blue')
# Display the image
image.show()
return len(blocks)
def main():
bucket = ''
document = ''
block_count=process_text_analysis(bucket,document)
print("Blocks detected: " + str(block_count))
if __name__ == "__main__":
main()
```
------
#### [ Node.js ]
以下範例程式碼會顯示檢測到的項目周圍的文件和框。
在下面的代碼中，替換`bucket`和`photo`以您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與文件名稱來取代和。替換`region`以及與帳户相關聯的地區。
```
// Import required AWS SDK clients and commands for Node.js
import { AnalyzeDocumentCommand } from "@aws-sdk/client-textract";
import { TextractClient } from "@aws-sdk/client-textract";
// Set the AWS Region.
const REGION = "region"; //e.g. "us-east-1"
// Create SNS service object.
const textractClient = new TextractClient({ region: REGION });
const bucket = 'buckets'
const photo = 'photo'
// Set params
const params = {
Document: {
S3Object: {
Bucket: bucket,
Name: photo
},
},
FeatureTypes: ['TABLES', 'FORMS'],
}
const displayBlockInfo = async (response) => {
try {
response.Blocks.forEach(block => {
console.log(`ID: ${block.Id}`)
console.log(`Block Type: ${block.BlockType}`)
if ("Text" in block && block.Text !== undefined){
console.log(`Text: ${block.Text}`)
}
else{}
if ("Confidence" in block && block.Confidence !== undefined){
console.log(`Confidence: ${block.Confidence}`)
}
else{}
if (block.BlockType == 'CELL'){
console.log("Cell info:")
console.log(` Column Index - ${block.ColumnIndex}`)
console.log(` Row - ${block.RowIndex}`)
console.log(` Column Span - ${block.ColumnSpan}`)
console.log(` Row Span - ${block.RowSpan}`)
}
if ("Relationships" in block && block.Relationships !== undefined){
console.log(block.Relationships)
console.log("Geometry:")
console.log(` Bounding Box - ${JSON.stringify(block.Geometry.BoundingBox)}`)
console.log(` Polygon - ${JSON.stringify(block.Geometry.Polygon)}`)
}
console.log("-----")
});
} catch (err) {
console.log("Error", err);
}
}
const analyze_document_text = async () => {
try {
const analyzeDoc = new AnalyzeDocumentCommand(params);
const response = await textractClient.send(analyzeDoc);
//console.log(response)
displayBlockInfo(response)
return response; // For unit tests.
} catch (err) {
console.log("Error", err);
}
}
analyze_document_text()
```
------
1. 執行範例。Python 和 Java 示例顯示帶有以下彩色邊界框的文檔圖像：
+ 紅色 — 密鑰塊對象
+ 綠色 — 值塊對象
+ 藍色 — 表格塊對象
+ 黃色 — 單元格塊對象
選取的選擇元素將用藍色填充。
所以此AWS CLI示例僅顯示`AnalyzeDocument`operation.
# 使用 Amazon Textract 分析發票和收據
要分析發票和收據單據，您可以使用分析費用 API，並傳遞文檔文件作為輸入。`AnalyzeExpense`是返回包含分析文本的 JSON 結構的同步操作。如需詳細資訊，請參閱 [分析發票和收款](invoices-receipts.md)。
要異步分析發票和收據，請使用`StartExpenseAnalysis`開始處理輸入文檔文件並使用`GetExpenseAnalysis`取得結果。
您提供的輸入文檔可以是影像位元組陣列 (base64 編碼影像位元組)，或者 Amazon S3 物件。在此步驟中，您會將影像檔案上傳至您的 S3 儲存貯體，並指定檔案名稱。
**分析發票或收據 (API)**
1. 如果您尚未：
1. 使用創建或更新 IAM 用户`AmazonTextractFullAccess`和`AmazonS3ReadOnlyAccess`許可。如需詳細資訊，請參閱 [步驟 1：設定 AWS 帳户並建立 IAM 使用者](setting-up.md#setting-up-iam)。
1. 安裝並設定 AWS CLI 和 AWS SDK。如需詳細資訊，請參閱 [步驟 2：設定AWS CLI和AWS開發套件](setup-awscli-sdk.md)。
1. 將含有文檔的影像上傳至您的 S3 儲存貯體。
如需說明，請參閱「」[將物件上傳至 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)中的*Amazon Simple Storage Service 用户指南*。
1. 使用下列範例來呼叫 `AnalyzeExpense` 操作。
------
#### [ CLI ]
```
aws textract analyze-expense --document '{"S3Object": {"Bucket": "bucket name","Name":
"object name"}}'
```
------
#### [ Python ]
```
import boto3
import io
from PIL import Image, ImageDraw
def draw_bounding_box(key, val, width, height, draw):
# If a key is Geometry, draw the bounding box info in it
if "Geometry" in key:
# Draw bounding box information
box = val["BoundingBox"]
left = width * box['Left']
top = height * box['Top']
draw.rectangle([left, top, left + (width * box['Width']), top + (height * box['Height'])],
outline='black')
# Takes a field as an argument and prints out the detected labels and values
def print_labels_and_values(field):
# Only if labels are detected and returned
if "LabelDetection" in field:
print("Summary Label Detection - Confidence: {}".format(
str(field.get("LabelDetection")["Confidence"])) + ", "
+ "Summary Values: {}".format(str(field.get("LabelDetection")["Text"])))
print(field.get("LabelDetection")["Geometry"])
else:
print("Label Detection - No labels returned.")
if "ValueDetection" in field:
print("Summary Value Detection - Confidence: {}".format(
str(field.get("ValueDetection")["Confidence"])) + ", "
+ "Summary Values: {}".format(str(field.get("ValueDetection")["Text"])))
print(field.get("ValueDetection")["Geometry"])
else:
print("Value Detection - No values returned")
def process_text_detection(bucket, document):
# Get the document from S3
s3_connection = boto3.resource('s3')
s3_object = s3_connection.Object(bucket, document)
s3_response = s3_object.get()
# opening binary stream using an in-memory bytes buffer
stream = io.BytesIO(s3_response['Body'].read())
# loading stream into image
image = Image.open(stream)
# Detect text in the document
client = boto3.client('textract', region_name="us-east-1")
# process using S3 object
response = client.analyze_expense(
Document={'S3Object': {'Bucket': bucket, 'Name': document}})
# Set width and height to display image and draw bounding boxes
# Create drawing object
width, height = image.size
draw = ImageDraw.Draw(image)
for expense_doc in response["ExpenseDocuments"]:
for line_item_group in expense_doc["LineItemGroups"]:
for line_items in line_item_group["LineItems"]:
for expense_fields in line_items["LineItemExpenseFields"]:
print_labels_and_values(expense_fields)
print()
print("Summary:")
for summary_field in expense_doc["SummaryFields"]:
print_labels_and_values(summary_field)
print()
#For draw bounding boxes
for line_item_group in expense_doc["LineItemGroups"]:
for line_items in line_item_group["LineItems"]:
for expense_fields in line_items["LineItemExpenseFields"]:
for key, val in expense_fields["ValueDetection"].items():
if "Geometry" in key:
draw_bounding_box(key, val, width, height, draw)
for label in expense_doc["SummaryFields"]:
if "LabelDetection" in label:
for key, val in label["LabelDetection"].items():
draw_bounding_box(key, val, width, height, draw)
# Display the image
image.show()
def main():
bucket = 'Bucket-Name'
document = 'Document-Name'
process_text_detection(bucket, document)
if __name__ == "__main__":
main()
```
------
#### [ Java ]
```
package com.amazonaws.samples;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.util.List;
import java.util.concurrent.CompletableFuture;
import javax.imageio.ImageIO;
import javax.swing.*;
import software.amazon.awssdk.auth.credentials.AwsBasicCredentials;
import software.amazon.awssdk.auth.credentials.StaticCredentialsProvider;
import software.amazon.awssdk.core.ResponseBytes;
import software.amazon.awssdk.core.async.AsyncResponseTransformer;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.s3.*;
import software.amazon.awssdk.services.s3.model.GetObjectRequest;
import software.amazon.awssdk.services.s3.model.GetObjectResponse;
import software.amazon.awssdk.services.textract.TextractClient;
import software.amazon.awssdk.services.textract.model.AnalyzeExpenseRequest;
import software.amazon.awssdk.services.textract.model.AnalyzeExpenseResponse;
import software.amazon.awssdk.services.textract.model.BoundingBox;
import software.amazon.awssdk.services.textract.model.Document;
import software.amazon.awssdk.services.textract.model.ExpenseDocument;
import software.amazon.awssdk.services.textract.model.ExpenseField;
import software.amazon.awssdk.services.textract.model.LineItemFields;
import software.amazon.awssdk.services.textract.model.LineItemGroup;
import software.amazon.awssdk.services.textract.model.S3Object;
import software.amazon.awssdk.services.textract.model.Point;
/**
*
* Demo code to parse Textract AnalyzeExpense API
*
*/
public class TextractAnalyzeExpenseSample extends JPanel {
private static final long serialVersionUID = 1L;
BufferedImage image;
static AnalyzeExpenseResponse result;
public TextractAnalyzeExpenseSample(AnalyzeExpenseResponse documentResult, BufferedImage bufImage) throws Exception {
super();
result = documentResult; // Results of analyzeexpense summaryfields and lineitemgroups detection.
image = bufImage; // The image containing the document.
}
// Draws the image and text bounding box.
public void paintComponent(Graphics g) {
Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g.
// Draw the image.
g2d.drawImage(image, 0, 0, image.getWidth(this), image.getHeight(this), this);
// Iterate through summaryfields and lineitemgroups and display boundedboxes around lines of detected label and value.
List expenseDocuments = result.expenseDocuments();
for (ExpenseDocument expenseDocument : expenseDocuments) {
if (expenseDocument.hasSummaryFields()) {
DisplayAnalyzeExpenseSummaryInfo(expenseDocument);
List summaryfields = expenseDocument.summaryFields();
for (ExpenseField summaryfield : summaryfields) {
if (summaryfield.valueDetection() != null) {
ShowBoundingBox(image.getHeight(this), image.getWidth(this),
summaryfield.valueDetection().geometry().boundingBox(), g2d, new Color(0, 0, 0));
}
if (summaryfield.labelDetection() != null) {
ShowBoundingBox(image.getHeight(this), image.getWidth(this),
summaryfield.labelDetection().geometry().boundingBox(), g2d, new Color(0, 0, 0));
}
}
}
if (expenseDocument.hasLineItemGroups()) {
DisplayAnalyzeExpenseLineItemGroupsInfo(expenseDocument);
List lineitemgroups = expenseDocument.lineItemGroups();
for (LineItemGroup lineitemgroup : lineitemgroups) {
if (lineitemgroup.hasLineItems()) {
List lineItems = lineitemgroup.lineItems();
for (LineItemFields lineitemfield : lineItems) {
if (lineitemfield.hasLineItemExpenseFields()) {
List expensefields = lineitemfield.lineItemExpenseFields();
for (ExpenseField expensefield : expensefields) {
if (expensefield.valueDetection() != null) {
ShowBoundingBox(image.getHeight(this), image.getWidth(this),
expensefield.valueDetection().geometry().boundingBox(), g2d,
new Color(0, 0, 0));
}
if (expensefield.labelDetection() != null) {
ShowBoundingBox(image.getHeight(this), image.getWidth(this),
expensefield.labelDetection().geometry().boundingBox(), g2d,
new Color(0, 0, 0));
}
}
}
}
}
}
}
}
}
// Show bounding box at supplied location.
private void ShowBoundingBox(float imageHeight, float imageWidth, BoundingBox box, Graphics2D g2d, Color color) {
float left = imageWidth * box.left();
float top = imageHeight * box.top();
// Display bounding box.
g2d.setColor(color);
g2d.drawRect(Math.round(left), Math.round(top), Math.round(imageWidth * box.width()),
Math.round(imageHeight * box.height()));
}
private void ShowSelectedElement(float imageHeight, float imageWidth, BoundingBox box, Graphics2D g2d,
Color color) {
float left = (float) imageWidth * (float) box.left();
float top = (float) imageHeight * (float) box.top();
System.out.println(left);
System.out.println(top);
// Display bounding box.
g2d.setColor(color);
g2d.fillRect(Math.round(left), Math.round(top), Math.round(imageWidth * box.width()),
Math.round(imageHeight * box.height()));
}
// Shows polygon at supplied location
private void ShowPolygon(int imageHeight, int imageWidth, List points, Graphics2D g2d) {
g2d.setColor(new Color(0, 0, 0));
Polygon polygon = new Polygon();
// Construct polygon and display
for (Point point : points) {
polygon.addPoint((Math.round(point.x() * imageWidth)), Math.round(point.y() * imageHeight));
}
g2d.drawPolygon(polygon);
}
private void DisplayAnalyzeExpenseSummaryInfo(ExpenseDocument expensedocument) {
System.out.println(" ExpenseId : " + expensedocument.expenseIndex());
System.out.println(" Expense Summary information:");
if (expensedocument.hasSummaryFields()) {
List summaryfields = expensedocument.summaryFields();
for (ExpenseField summaryfield : summaryfields) {
System.out.println(" Page: " + summaryfield.pageNumber());
if (summaryfield.type() != null) {
System.out.println(" Expense Summary Field Type:" + summaryfield.type().text());
}
if (summaryfield.labelDetection() != null) {
System.out.println(" Expense Summary Field Label:" + summaryfield.labelDetection().text());
System.out.println(" Geometry");
System.out.println(" Bounding Box: "
+ summaryfield.labelDetection().geometry().boundingBox().toString());
System.out.println(
" Polygon: " + summaryfield.labelDetection().geometry().polygon().toString());
}
if (summaryfield.valueDetection() != null) {
System.out.println(" Expense Summary Field Value:" + summaryfield.valueDetection().text());
System.out.println(" Geometry");
System.out.println(" Bounding Box: "
+ summaryfield.valueDetection().geometry().boundingBox().toString());
System.out.println(
" Polygon: " + summaryfield.valueDetection().geometry().polygon().toString());
}
}
}
}
private void DisplayAnalyzeExpenseLineItemGroupsInfo(ExpenseDocument expensedocument) {
System.out.println(" ExpenseId : " + expensedocument.expenseIndex());
System.out.println(" Expense LineItemGroups information:");
if (expensedocument.hasLineItemGroups()) {
List lineitemgroups = expensedocument.lineItemGroups();
for (LineItemGroup lineitemgroup : lineitemgroups) {
System.out.println(" Expense LineItemGroupsIndexID :" + lineitemgroup.lineItemGroupIndex());
if (lineitemgroup.hasLineItems()) {
List lineItems = lineitemgroup.lineItems();
for (LineItemFields lineitemfield : lineItems) {
if (lineitemfield.hasLineItemExpenseFields()) {
List expensefields = lineitemfield.lineItemExpenseFields();
for (ExpenseField expensefield : expensefields) {
if (expensefield.type() != null) {
System.out.println(" Expense LineItem Field Type:" + expensefield.type().text());
}
if (expensefield.valueDetection() != null) {
System.out.println(
" Expense Summary Field Value:" + expensefield.valueDetection().text());
System.out.println(" Geometry");
System.out.println(" Bounding Box: "
+ expensefield.valueDetection().geometry().boundingBox().toString());
System.out.println(" Polygon: "
+ expensefield.valueDetection().geometry().polygon().toString());
}
if (expensefield.labelDetection() != null) {
System.out.println(
" Expense LineItem Field Label:" + expensefield.labelDetection().text());
System.out.println(" Geometry");
System.out.println(" Bounding Box: "
+ expensefield.labelDetection().geometry().boundingBox().toString());
System.out.println(" Polygon: "
+ expensefield.labelDetection().geometry().polygon().toString());
}
}
}
}
}
}
}
}
public static void main(String arg[]) throws Exception {
// Creates a default async client with credentials and AWS Region loaded from
// the
// environment
S3AsyncClient client = S3AsyncClient.builder().region(Region.US_EAST_1).build();
System.out.println("Creating the S3 Client");
// Start the call to Amazon S3, not blocking to wait for the result
CompletableFuture> responseFuture = client.getObject(
GetObjectRequest.builder().bucket("textractanalyzeexpense").key("input/sample-receipt.jpg").build(),
AsyncResponseTransformer.toBytes());
System.out.println("Successfully read the object");
// When future is complete (either successfully or in error), handle the
// response
CompletableFuture> operationCompleteFuture = responseFuture
.whenComplete((getObjectResponse, exception) -> {
if (getObjectResponse != null) {
// At this point, the file my-file.out has been created with the data
// from S3; let's just print the object version
// Convert this into Async call and remove the below block from here and put it
// outside
TextractClient textractclient = TextractClient.builder().region(Region.US_EAST_1).build();
AnalyzeExpenseRequest request = AnalyzeExpenseRequest.builder()
.document(
Document.builder().s3Object(S3Object.builder().name("YOURObjectName")
.bucket("YOURBucket").build()).build())
.build();
AnalyzeExpenseResponse result = textractclient.analyzeExpense(request);
System.out.print(result.toString());
ByteArrayInputStream bais = new ByteArrayInputStream(getObjectResponse.asByteArray());
try {
BufferedImage image = ImageIO.read(bais);
System.out.println("Successfully read the image");
JFrame frame = new JFrame("Expense Image");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
TextractAnalyzeExpense panel = new TextractAnalyzeExpense(result, image);
panel.setPreferredSize(new Dimension(image.getWidth(), image.getHeight()));
frame.setContentPane(panel);
frame.pack();
frame.setVisible(true);
} catch (IOException e) {
throw new RuntimeException(e);
} catch (Exception e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
} else {
// Handle the error
exception.printStackTrace();
}
});
// We could do other work while waiting for the AWS call to complete in
// the background, but we'll just wait for "whenComplete" to finish instead
operationCompleteFuture.join();
}
}
```
------
#### [ Node.Js ]
```
// Import required AWS SDK clients and commands for Node.js
import { AnalyzeExpenseCommand } from "@aws-sdk/client-textract";
import { TextractClient } from "@aws-sdk/client-textract";
// Set the AWS Region.
const REGION = "region"; //e.g. "us-east-1"
// Create SNS service object.
const textractClient = new TextractClient({ region: REGION });
const bucket = 'bucket'
const photo = 'photo'
// Set params
const params = {
Document: {
S3Object: {
Bucket: bucket,
Name: photo
},
},
}
const process_text_detection = async () => {
try {
const aExpense = new AnalyzeExpenseCommand(params);
const response = await textractClient.send(aExpense);
//console.log(response)
response.ExpenseDocuments.forEach(doc => {
doc.LineItemGroups.forEach(items => {
items.LineItems.forEach(fields => {
fields.LineItemExpenseFields.forEach(expenseFields =>{
console.log(expenseFields)
})
}
)}
)
}
)
return response; // For unit tests.
} catch (err) {
console.log("Error", err);
}
}
process_text_detection()
```
------
1. 這將為您提供`AnalyzeExpense`operation.
# 使用 Amazon Textract 分析身份文檔
要分析身份文檔，請使用 AnalyzeID API，並將文檔文件作為輸入傳遞。`AnalyzeID`會傳回含有分析文字的 JSON 結構。如需詳細資訊，請參閱 [分析身分文件](how-it-works-identity.md)。
您提供的輸入文檔可以是影像位元組陣列 (base64 編碼影像位元組)，或者 Amazon S3 物件。在此步驟中，您會將影像檔案上傳至您的 S3 儲存貯體，並指定檔案名稱。
**分析身份文檔 (API)**
1. 如果您尚未：
1. 使用創建或更新 IAM 用户`AmazonTextractFullAccess`和`AmazonS3ReadOnlyAccess`許可。如需詳細資訊，請參閱 [步驟 1：設定 AWS 帳户並建立 IAM 使用者](setting-up.md#setting-up-iam)。
1. 安裝並設定 AWS CLI 和 AWS SDK。如需詳細資訊，請參閱 [步驟 2：設定AWS CLI和AWS開發套件](setup-awscli-sdk.md)。
1. 將含有文檔的影像上傳至您的 S3 儲存貯體。
如需說明，請參閱「」[將物件上傳至 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)中的*Amazon Simple Storage Service 用户指南*。
1. 使用下列範例來呼叫 `AnalyzeID` 操作。
------
#### [ CLI ]
下列範例從 S3 儲存貯體傳入文件，並運行`AnalyzeID`操作。在下面的代碼中，替換*桶*使用 S3 儲存貯體的名稱，則*文件*添加儲存貯體中的檔案名稱，以及*區域*的名稱`region`與您的帳户相關聯。
```
aws textract analyze-id --document-pages '[{"S3Object":{"Bucket":"bucket","Name":"name"}}]' --region region
```
您也可以通過向輸入添加另一個 S3 對象，使用駕駛執照的正面和背面調用 API。
```
aws textract analyze-id --document-pages '[{"S3Object":{"Bucket":"bucket","Name":"name front"}}, {"S3Object":{"Bucket":"bucket","Name":"name back"}}]' --region us-east-1
```
如果您正在 Windows 設備上訪問 CLI，請使用雙引號而不是單引號，並用反斜槓（即\$1）轉義內部雙引號以解決您可能遇到的任何解析器錯誤。舉例來講，請參閲下列內容：
```
aws textract analyze-id --document-pages "[{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"name\"}}]" --region region
```
------
#### [ Python ]
下列範例從 S3 儲存貯體傳入文件，並運行`AnalyzeID`操作，返回檢測到的鍵值組。在下面的代碼中，替換*bucket\$1name*與您 S3 儲存貯體的名稱一起使用，*文件名*添加儲存貯體中的檔案名稱，以及*區域*的名稱`region`與您的帳户相關聯。
```
import boto3
bucket_name = "bucket-name"
file_name = "file-name"
region = "region-name"
def analyze_id(region, bucket_name, file_name):
textract_client = boto3.client('textract', region_name=region)
response = textract_client.analyze_id(DocumentPages=[{"S3Object":{"Bucket":bucket_name,"Name":file_name}}])
for doc_fields in response['IdentityDocuments']:
for id_field in doc_fields['IdentityDocumentFields']:
for key, val in id_field.items():
if "Type" in str(key):
print("Type: " + str(val['Text']))
for key, val in id_field.items():
if "ValueDetection" in str(key):
print("Value Detection: " + str(val['Text']))
print()
analyze_id(region, bucket_name, file_name)
```
------
#### [ Java ]
下列範例從 S3 儲存貯體傳入文件，並運行`AnalyzeID`操作，返回檢測到的數據。在函數 main 中，將`s3bucket`和`sourceDoc`以您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與文件影像名稱來取代和。
```
/*
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: Apache-2.0
*/
package com.amazonaws.samples;
import com.amazonaws.regions.Regions;
import com.amazonaws.services.textract.AmazonTextractClient;
import com.amazonaws.services.textract.AmazonTextractClientBuilder;
import com.amazonaws.services.textract.model.*;
import java.util.ArrayList;
import java.util.List;
public class AnalyzeIdentityDocument {
public static void main(String[] args) {
final String USAGE = "\n" +
"Usage:\n" +
" \n\n" +
"Where:\n" +
" s3bucket - the Amazon S3 bucket where the document is located. \n" +
" sourceDoc - the name of the document. \n";
if (args.length != 1) {
System.out.println(USAGE);
System.exit(1);
}
String s3bucket = "bucket-name"; //args[0];
String sourceDoc = "sourcedoc-name"; //args[1];
AmazonTextractClient textractClient = (AmazonTextractClient) AmazonTextractClientBuilder.standard()
.withRegion(Regions.US_EAST_1)
.build();
getDocDetails(textractClient, s3bucket, sourceDoc);
}
public static void getDocDetails(AmazonTextractClient textractClient, String s3bucket, String sourceDoc ) {
try {
S3Object s3 = new S3Object();
s3.setBucket(s3bucket);
s3.setName(sourceDoc);
com.amazonaws.services.textract.model.Document myDoc = new com.amazonaws.services.textract.model.Document();
myDoc.setS3Object(s3);
List list1 = new ArrayList();
list1.add(myDoc);
AnalyzeIDRequest idRequest = new AnalyzeIDRequest();
idRequest.setDocumentPages(list1);
AnalyzeIDResult result = textractClient.analyzeID(idRequest);
List docs = result.getIdentityDocuments();
for (IdentityDocument doc: docs) {
ListidFields = doc.getIdentityDocumentFields();
for (IdentityDocumentField field: idFields) {
System.out.println("Field type is "+ field.getType().getText());
System.out.println("Field value is "+ field.getValueDetection().getText());
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
}
```
------
1. 這將為您提供`AnalyzeID`operation.