您可以使用 Firebase ML 來識別圖像中的文字。 Firebase ML 既有適合識別影像中文字（例如路標文字）的通用 API，也有針對識別文件文字而最佳化的 API。
在你開始之前
- 如果您尚未將 Firebase 新增至您的 Android 專案中，請將其新增至您的 Android 專案中。
- 在模組（應用程式等級）Gradle 檔案（通常
<project>/<app-module>/build.gradle.kts或<project>/<app-module>/build.gradle）中，新增 Firebase ML 的依賴項Android視覺庫。我們建議使用Firebase Android BoM來控制函式庫版本控制。dependencies { // Import the BoM for the Firebase platform implementation(platform("com.google.firebase:firebase-bom:32.8.0")) // Add the dependency for the Firebase ML Vision library // When using the BoM, you don't specify versions in Firebase library dependencies implementation 'com.google.firebase:firebase-ml-vision' }透過使用Firebase Android BoM ，您的應用程式將始終使用 Firebase Android 程式庫的相容版本。
（替代方法）在不使用 BoM 的情況下新增 Firebase 庫依賴項
如果您選擇不使用 Firebase BoM，則必須在其依賴項行中指定每個 Firebase 庫版本。
請注意，如果您在應用程式中使用多個Firebase 程式庫，我們強烈建議使用 BoM 來管理程式庫版本，這可確保所有版本相容。
dependencies { // Add the dependency for the Firebase ML Vision library // When NOT using the BoM, you must specify versions in Firebase library dependencies implementation 'com.google.firebase:firebase-ml-vision:24.1.0' } 如果您尚未為您的專案啟用基於雲端的 API，請立即執行此操作：
- 開啟 Firebase 控制台的Firebase ML API 頁面。
如果您尚未將項目升級到 Blaze 定價計劃，請按一下升級來執行此操作。 （只有當您的專案不在 Blaze 計劃中時，系統才會提示您升級。）
只有 Blaze 等級的項目才能使用基於雲端的 API。
- 如果尚未啟用基於雲端的 API，請按一下啟用基於雲端的 API 。
現在您已準備好開始識別圖像中的文字。
輸入影像指南
為了讓 Firebase ML 準確地識別文本，輸入圖像必須包含由足夠的像素資料表示的文本。理想情況下，對於拉丁文本，每個字元應至少為 16x16 像素。對於中文、日文和韓文文本，每個字元應為 24x24 像素。對於所有語言，大於 24x24 像素的字元通常不會帶來準確性優勢。
例如，640x480 的影像可能適合掃描佔據影像整個寬度的名片。要掃描列印在 letter 尺寸紙張上的文檔，可能需要 720x1280 像素的圖像。
影像焦點不佳會損害文字辨識的準確性。如果您沒有獲得可接受的結果，請嘗試要求使用者重新捕捉影像。
辨識圖像中的文字
若要識別圖像中的文本，請按如下所述運行文字辨識器。
1. 運行文字辨識器
要識別圖像中的文本，請從Bitmap 、 media.Image 、 ByteBuffer 、位元組數組或裝置上的檔案建立FirebaseVisionImage物件。然後，將FirebaseVisionImage物件傳遞給FirebaseVisionTextRecognizer的processImage方法。從您的映像建立
FirebaseVisionImage物件。若要從
media.Image物件建立FirebaseVisionImage物件（例如從裝置的相機擷取影像時），請將media.Image物件和影像的旋轉傳遞給FirebaseVisionImage.fromMediaImage()。如果您使用CameraX函式庫，則
OnImageCapturedListener和ImageAnalysis.Analyzer類別會為您計算旋轉值，因此您只需在呼叫FirebaseVisionImage.fromMediaImage()之前將旋轉轉換為 Firebase ML 的ROTATION_常數之一：Kotlin+KTX
private class YourImageAnalyzer : ImageAnalysis.Analyzer { private fun degreesToFirebaseRotation(degrees: Int): Int = when(degrees) { 0 -> FirebaseVisionImageMetadata.ROTATION_0 90 -> FirebaseVisionImageMetadata.ROTATION_90 180 -> FirebaseVisionImageMetadata.ROTATION_180 270 -> FirebaseVisionImageMetadata.ROTATION_270 else -> throw Exception("Rotation must be 0, 90, 180, or 270.") } override fun analyze(imageProxy: ImageProxy?, degrees: Int) { val mediaImage = imageProxy?.image val imageRotation = degreesToFirebaseRotation(degrees) if (mediaImage != null) { val image = FirebaseVisionImage.fromMediaImage(mediaImage, imageRotation) // Pass image to an ML Vision API // ... } } }Java
private class YourAnalyzer implements ImageAnalysis.Analyzer { private int degreesToFirebaseRotation(int degrees) { switch (degrees) { case 0: return FirebaseVisionImageMetadata.ROTATION_0; case 90: return FirebaseVisionImageMetadata.ROTATION_90; case 180: return FirebaseVisionImageMetadata.ROTATION_180; case 270: return FirebaseVisionImageMetadata.ROTATION_270; default: throw new IllegalArgumentException( "Rotation must be 0, 90, 180, or 270."); } } @Override public void analyze(ImageProxy imageProxy, int degrees) { if (imageProxy == null || imageProxy.getImage() == null) { return; } Image mediaImage = imageProxy.getImage(); int rotation = degreesToFirebaseRotation(degrees); FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation); // Pass image to an ML Vision API // ... } }如果您不使用為您提供影像旋轉的相機庫，您可以根據裝置的旋轉和裝置中相機感測器的方向來計算它：
Kotlin+KTX
private val ORIENTATIONS = SparseIntArray() init { ORIENTATIONS.append(Surface.ROTATION_0, 90) ORIENTATIONS.append(Surface.ROTATION_90, 0) ORIENTATIONS.append(Surface.ROTATION_180, 270) ORIENTATIONS.append(Surface.ROTATION_270, 180) } /** * Get the angle by which an image must be rotated given the device's current * orientation. */ @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP) @Throws(CameraAccessException::class) private fun getRotationCompensation(cameraId: String, activity: Activity, context: Context): Int { // Get the device's current rotation relative to its "native" orientation. // Then, from the ORIENTATIONS table, look up the angle the image must be // rotated to compensate for the device's rotation. val deviceRotation = activity.windowManager.defaultDisplay.rotation var rotationCompensation = ORIENTATIONS.get(deviceRotation) // On most devices, the sensor orientation is 90 degrees, but for some // devices it is 270 degrees. For devices with a sensor orientation of // 270, rotate the image an additional 180 ((270 + 270) % 360) degrees. val cameraManager = context.getSystemService(CAMERA_SERVICE) as CameraManager val sensorOrientation = cameraManager .getCameraCharacteristics(cameraId) .get(CameraCharacteristics.SENSOR_ORIENTATION)!! rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360 // Return the corresponding FirebaseVisionImageMetadata rotation value. val result: Int when (rotationCompensation) { 0 -> result = FirebaseVisionImageMetadata.ROTATION_0 90 -> result = FirebaseVisionImageMetadata.ROTATION_90 180 -> result = FirebaseVisionImageMetadata.ROTATION_180 270 -> result = FirebaseVisionImageMetadata.ROTATION_270 else -> { result = FirebaseVisionImageMetadata.ROTATION_0 Log.e(TAG, "Bad rotation value: $rotationCompensation") } } return result }Java
private static final SparseIntArray ORIENTATIONS = new SparseIntArray(); static { ORIENTATIONS.append(Surface.ROTATION_0, 90); ORIENTATIONS.append(Surface.ROTATION_90, 0); ORIENTATIONS.append(Surface.ROTATION_180, 270); ORIENTATIONS.append(Surface.ROTATION_270, 180); } /** * Get the angle by which an image must be rotated given the device's current * orientation. */ @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP) private int getRotationCompensation(String cameraId, Activity activity, Context context) throws CameraAccessException { // Get the device's current rotation relative to its "native" orientation. // Then, from the ORIENTATIONS table, look up the angle the image must be // rotated to compensate for the device's rotation. int deviceRotation = activity.getWindowManager().getDefaultDisplay().getRotation(); int rotationCompensation = ORIENTATIONS.get(deviceRotation); // On most devices, the sensor orientation is 90 degrees, but for some // devices it is 270 degrees. For devices with a sensor orientation of // 270, rotate the image an additional 180 ((270 + 270) % 360) degrees. CameraManager cameraManager = (CameraManager) context.getSystemService(CAMERA_SERVICE); int sensorOrientation = cameraManager .getCameraCharacteristics(cameraId) .get(CameraCharacteristics.SENSOR_ORIENTATION); rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360; // Return the corresponding FirebaseVisionImageMetadata rotation value. int result; switch (rotationCompensation) { case 0: result = FirebaseVisionImageMetadata.ROTATION_0; break; case 90: result = FirebaseVisionImageMetadata.ROTATION_90; break; case 180: result = FirebaseVisionImageMetadata.ROTATION_180; break; case 270: result = FirebaseVisionImageMetadata.ROTATION_270; break; default: result = FirebaseVisionImageMetadata.ROTATION_0; Log.e(TAG, "Bad rotation value: " + rotationCompensation); } return result; }然後，將
media.Image物件和旋轉值傳遞給FirebaseVisionImage.fromMediaImage()：Kotlin+KTX
val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
- 若要從檔案 URI 建立
FirebaseVisionImage對象，請將套用上下文和檔案 URI 傳遞給FirebaseVisionImage.fromFilePath()。當您使用ACTION_GET_CONTENT意圖提示使用者從其圖庫應用程式中選擇影像時，這非常有用。Kotlin+KTX
val image: FirebaseVisionImage try { image = FirebaseVisionImage.fromFilePath(context, uri) } catch (e: IOException) { e.printStackTrace() }Java
FirebaseVisionImage image; try { image = FirebaseVisionImage.fromFilePath(context, uri); } catch (IOException e) { e.printStackTrace(); } - 若要從
ByteBuffer或位元組數組建立FirebaseVisionImage對象，請先按照上面針對media.Image輸入所述計算圖像旋轉。然後，建立一個
FirebaseVisionImageMetadata對象，其中包含圖像的高度、寬度、顏色編碼格式和旋轉：Kotlin+KTX
val metadata = FirebaseVisionImageMetadata.Builder() .setWidth(480) // 480x360 is typically sufficient for .setHeight(360) // image recognition .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21) .setRotation(rotation) .build()Java
FirebaseVisionImageMetadata metadata = new FirebaseVisionImageMetadata.Builder() .setWidth(480) // 480x360 is typically sufficient for .setHeight(360) // image recognition .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21) .setRotation(rotation) .build();使用緩衝區或陣列以及元資料物件來建立
FirebaseVisionImage物件：Kotlin+KTX
val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata) // Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata); // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
- 要從
Bitmap物件建立FirebaseVisionImage物件：Kotlin+KTX
val image = FirebaseVisionImage.fromBitmap(bitmap)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
Bitmap物件表示的影像必須是直立的，不需要額外旋轉。
取得
FirebaseVisionTextRecognizer的實例。Kotlin+KTX
val detector = FirebaseVision.getInstance().cloudTextRecognizer // Or, to change the default settings: // val detector = FirebaseVision.getInstance().getCloudTextRecognizer(options)
// Or, to provide language hints to assist with language detection: // See https://cloud.google.com/vision/docs/languages for supported languages val options = FirebaseVisionCloudTextRecognizerOptions.Builder() .setLanguageHints(listOf("en", "hi")) .build()Java
FirebaseVisionTextRecognizer detector = FirebaseVision.getInstance() .getCloudTextRecognizer(); // Or, to change the default settings: // FirebaseVisionTextRecognizer detector = FirebaseVision.getInstance() // .getCloudTextRecognizer(options);// Or, to provide language hints to assist with language detection: // See https://cloud.google.com/vision/docs/languages for supported languages FirebaseVisionCloudTextRecognizerOptions options = new FirebaseVisionCloudTextRecognizerOptions.Builder() .setLanguageHints(Arrays.asList("en", "hi")) .build();最後，將圖像傳遞給
processImage方法：Kotlin+KTX
val result = detector.processImage(image) .addOnSuccessListener { firebaseVisionText -> // Task completed successfully // ... } .addOnFailureListener { e -> // Task failed with an exception // ... }Java
Task<FirebaseVisionText> result = detector.processImage(image) .addOnSuccessListener(new OnSuccessListener<FirebaseVisionText>() { @Override public void onSuccess(FirebaseVisionText firebaseVisionText) { // Task completed successfully // ... } }) .addOnFailureListener( new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { // Task failed with an exception // ... } });
2. 從識別的文本區塊中提取文本
如果文字辨識操作成功，FirebaseVisionText物件將被傳遞到成功偵聽器。 FirebaseVisionText物件包含影像中辨識的全文以及零個或多個TextBlock物件。每個TextBlock代表一個矩形文字區塊，其中包含零個或多個Line物件。每個Line對象包含零個或多個Element對象，這些對象表示單字和類似單字的實體（日期、數字等）。
對於每個TextBlock 、 Line和Element對象，您可以獲得該區域中識別的文字以及該區域的邊界座標。
例如：
Kotlin+KTX
val resultText = result.text
for (block in result.textBlocks) {
val blockText = block.text
val blockConfidence = block.confidence
val blockLanguages = block.recognizedLanguages
val blockCornerPoints = block.cornerPoints
val blockFrame = block.boundingBox
for (line in block.lines) {
val lineText = line.text
val lineConfidence = line.confidence
val lineLanguages = line.recognizedLanguages
val lineCornerPoints = line.cornerPoints
val lineFrame = line.boundingBox
for (element in line.elements) {
val elementText = element.text
val elementConfidence = element.confidence
val elementLanguages = element.recognizedLanguages
val elementCornerPoints = element.cornerPoints
val elementFrame = element.boundingBox
}
}
}Java
String resultText = result.getText();
for (FirebaseVisionText.TextBlock block: result.getTextBlocks()) {
String blockText = block.getText();
Float blockConfidence = block.getConfidence();
List<RecognizedLanguage> blockLanguages = block.getRecognizedLanguages();
Point[] blockCornerPoints = block.getCornerPoints();
Rect blockFrame = block.getBoundingBox();
for (FirebaseVisionText.Line line: block.getLines()) {
String lineText = line.getText();
Float lineConfidence = line.getConfidence();
List<RecognizedLanguage> lineLanguages = line.getRecognizedLanguages();
Point[] lineCornerPoints = line.getCornerPoints();
Rect lineFrame = line.getBoundingBox();
for (FirebaseVisionText.Element element: line.getElements()) {
String elementText = element.getText();
Float elementConfidence = element.getConfidence();
List<RecognizedLanguage> elementLanguages = element.getRecognizedLanguages();
Point[] elementCornerPoints = element.getCornerPoints();
Rect elementFrame = element.getBoundingBox();
}
}
}下一步
- 在將使用雲端 API 的應用程式部署到生產環境之前，您應該採取一些額外的步驟來防止和減輕未經授權的 API 存取的影響。
識別文件圖像中的文字
若要識別文件的文本，請按如下所述配置並執行文件文字辨識器。
下面描述的文件文字辨識 API 提供了一個旨在更方便處理文件影像的介面。但是，如果您更喜歡FirebaseVisionTextRecognizer API 提供的介面，則可以透過將雲端文字辨識器配置為使用密集文字模型來使用它來掃描文件。
使用文件文字識別API：
1. 運行文字辨識器
要識別圖像中的文本，請從Bitmap 、 media.Image 、 ByteBuffer 、位元組數組或裝置上的檔案建立FirebaseVisionImage物件。然後，將FirebaseVisionImage物件傳遞給FirebaseVisionDocumentTextRecognizer的processImage方法。從您的映像建立
FirebaseVisionImage物件。若要從
media.Image物件建立FirebaseVisionImage物件（例如從裝置的相機擷取影像時），請將media.Image物件和影像的旋轉傳遞給FirebaseVisionImage.fromMediaImage()。如果您使用CameraX函式庫，則
OnImageCapturedListener和ImageAnalysis.Analyzer類別會為您計算旋轉值，因此您只需在呼叫FirebaseVisionImage.fromMediaImage()之前將旋轉轉換為 Firebase ML 的ROTATION_常數之一：Kotlin+KTX
private class YourImageAnalyzer : ImageAnalysis.Analyzer { private fun degreesToFirebaseRotation(degrees: Int): Int = when(degrees) { 0 -> FirebaseVisionImageMetadata.ROTATION_0 90 -> FirebaseVisionImageMetadata.ROTATION_90 180 -> FirebaseVisionImageMetadata.ROTATION_180 270 -> FirebaseVisionImageMetadata.ROTATION_270 else -> throw Exception("Rotation must be 0, 90, 180, or 270.") } override fun analyze(imageProxy: ImageProxy?, degrees: Int) { val mediaImage = imageProxy?.image val imageRotation = degreesToFirebaseRotation(degrees) if (mediaImage != null) { val image = FirebaseVisionImage.fromMediaImage(mediaImage, imageRotation) // Pass image to an ML Vision API // ... } } }Java
private class YourAnalyzer implements ImageAnalysis.Analyzer { private int degreesToFirebaseRotation(int degrees) { switch (degrees) { case 0: return FirebaseVisionImageMetadata.ROTATION_0; case 90: return FirebaseVisionImageMetadata.ROTATION_90; case 180: return FirebaseVisionImageMetadata.ROTATION_180; case 270: return FirebaseVisionImageMetadata.ROTATION_270; default: throw new IllegalArgumentException( "Rotation must be 0, 90, 180, or 270."); } } @Override public void analyze(ImageProxy imageProxy, int degrees) { if (imageProxy == null || imageProxy.getImage() == null) { return; } Image mediaImage = imageProxy.getImage(); int rotation = degreesToFirebaseRotation(degrees); FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation); // Pass image to an ML Vision API // ... } }如果您不使用為您提供影像旋轉的相機庫，您可以根據裝置的旋轉和裝置中相機感測器的方向來計算它：
Kotlin+KTX
private val ORIENTATIONS = SparseIntArray() init { ORIENTATIONS.append(Surface.ROTATION_0, 90) ORIENTATIONS.append(Surface.ROTATION_90, 0) ORIENTATIONS.append(Surface.ROTATION_180, 270) ORIENTATIONS.append(Surface.ROTATION_270, 180) } /** * Get the angle by which an image must be rotated given the device's current * orientation. */ @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP) @Throws(CameraAccessException::class) private fun getRotationCompensation(cameraId: String, activity: Activity, context: Context): Int { // Get the device's current rotation relative to its "native" orientation. // Then, from the ORIENTATIONS table, look up the angle the image must be // rotated to compensate for the device's rotation. val deviceRotation = activity.windowManager.defaultDisplay.rotation var rotationCompensation = ORIENTATIONS.get(deviceRotation) // On most devices, the sensor orientation is 90 degrees, but for some // devices it is 270 degrees. For devices with a sensor orientation of // 270, rotate the image an additional 180 ((270 + 270) % 360) degrees. val cameraManager = context.getSystemService(CAMERA_SERVICE) as CameraManager val sensorOrientation = cameraManager .getCameraCharacteristics(cameraId) .get(CameraCharacteristics.SENSOR_ORIENTATION)!! rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360 // Return the corresponding FirebaseVisionImageMetadata rotation value. val result: Int when (rotationCompensation) { 0 -> result = FirebaseVisionImageMetadata.ROTATION_0 90 -> result = FirebaseVisionImageMetadata.ROTATION_90 180 -> result = FirebaseVisionImageMetadata.ROTATION_180 270 -> result = FirebaseVisionImageMetadata.ROTATION_270 else -> { result = FirebaseVisionImageMetadata.ROTATION_0 Log.e(TAG, "Bad rotation value: $rotationCompensation") } } return result }Java
private static final SparseIntArray ORIENTATIONS = new SparseIntArray(); static { ORIENTATIONS.append(Surface.ROTATION_0, 90); ORIENTATIONS.append(Surface.ROTATION_90, 0); ORIENTATIONS.append(Surface.ROTATION_180, 270); ORIENTATIONS.append(Surface.ROTATION_270, 180); } /** * Get the angle by which an image must be rotated given the device's current * orientation. */ @RequiresApi(api = Build.VERSION_CODES.LOLLIPOP) private int getRotationCompensation(String cameraId, Activity activity, Context context) throws CameraAccessException { // Get the device's current rotation relative to its "native" orientation. // Then, from the ORIENTATIONS table, look up the angle the image must be // rotated to compensate for the device's rotation. int deviceRotation = activity.getWindowManager().getDefaultDisplay().getRotation(); int rotationCompensation = ORIENTATIONS.get(deviceRotation); // On most devices, the sensor orientation is 90 degrees, but for some // devices it is 270 degrees. For devices with a sensor orientation of // 270, rotate the image an additional 180 ((270 + 270) % 360) degrees. CameraManager cameraManager = (CameraManager) context.getSystemService(CAMERA_SERVICE); int sensorOrientation = cameraManager .getCameraCharacteristics(cameraId) .get(CameraCharacteristics.SENSOR_ORIENTATION); rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360; // Return the corresponding FirebaseVisionImageMetadata rotation value. int result; switch (rotationCompensation) { case 0: result = FirebaseVisionImageMetadata.ROTATION_0; break; case 90: result = FirebaseVisionImageMetadata.ROTATION_90; break; case 180: result = FirebaseVisionImageMetadata.ROTATION_180; break; case 270: result = FirebaseVisionImageMetadata.ROTATION_270; break; default: result = FirebaseVisionImageMetadata.ROTATION_0; Log.e(TAG, "Bad rotation value: " + rotationCompensation); } return result; }然後，將
media.Image物件和旋轉值傳遞給FirebaseVisionImage.fromMediaImage()：Kotlin+KTX
val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
- 若要從檔案 URI 建立
FirebaseVisionImage對象，請將套用上下文和檔案 URI 傳遞給FirebaseVisionImage.fromFilePath()。當您使用ACTION_GET_CONTENT意圖提示使用者從其圖庫應用程式中選擇影像時，這非常有用。Kotlin+KTX
val image: FirebaseVisionImage try { image = FirebaseVisionImage.fromFilePath(context, uri) } catch (e: IOException) { e.printStackTrace() }Java
FirebaseVisionImage image; try { image = FirebaseVisionImage.fromFilePath(context, uri); } catch (IOException e) { e.printStackTrace(); } - 若要從
ByteBuffer或位元組數組建立FirebaseVisionImage對象，請先按照上面針對media.Image輸入所述計算圖像旋轉。然後，建立一個
FirebaseVisionImageMetadata對象，其中包含圖像的高度、寬度、顏色編碼格式和旋轉：Kotlin+KTX
val metadata = FirebaseVisionImageMetadata.Builder() .setWidth(480) // 480x360 is typically sufficient for .setHeight(360) // image recognition .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21) .setRotation(rotation) .build()Java
FirebaseVisionImageMetadata metadata = new FirebaseVisionImageMetadata.Builder() .setWidth(480) // 480x360 is typically sufficient for .setHeight(360) // image recognition .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21) .setRotation(rotation) .build();使用緩衝區或陣列以及元資料物件來建立
FirebaseVisionImage物件：Kotlin+KTX
val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata) // Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata); // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
- 要從
Bitmap物件建立FirebaseVisionImage物件：Kotlin+KTX
val image = FirebaseVisionImage.fromBitmap(bitmap)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
Bitmap物件表示的影像必須是直立的，不需要額外的旋轉。
取得
FirebaseVisionDocumentTextRecognizer的實例：Kotlin+KTX
val detector = FirebaseVision.getInstance() .cloudDocumentTextRecognizer// Or, to provide language hints to assist with language detection: // See https://cloud.google.com/vision/docs/languages for supported languages val options = FirebaseVisionCloudDocumentRecognizerOptions.Builder() .setLanguageHints(listOf("en", "hi")) .build() val detector = FirebaseVision.getInstance() .getCloudDocumentTextRecognizer(options)Java
FirebaseVisionDocumentTextRecognizer detector = FirebaseVision.getInstance() .getCloudDocumentTextRecognizer();// Or, to provide language hints to assist with language detection: // See https://cloud.google.com/vision/docs/languages for supported languages FirebaseVisionCloudDocumentRecognizerOptions options = new FirebaseVisionCloudDocumentRecognizerOptions.Builder() .setLanguageHints(Arrays.asList("en", "hi")) .build(); FirebaseVisionDocumentTextRecognizer detector = FirebaseVision.getInstance() .getCloudDocumentTextRecognizer(options);最後，將圖像傳遞給
processImage方法：Kotlin+KTX
detector.processImage(myImage) .addOnSuccessListener { firebaseVisionDocumentText -> // Task completed successfully // ... } .addOnFailureListener { e -> // Task failed with an exception // ... }Java
detector.processImage(myImage) .addOnSuccessListener(new OnSuccessListener<FirebaseVisionDocumentText>() { @Override public void onSuccess(FirebaseVisionDocumentText result) { // Task completed successfully // ... } }) .addOnFailureListener(new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { // Task failed with an exception // ... } });
2. 從識別的文本區塊中提取文本
如果文字辨識操作成功，它將傳回一個FirebaseVisionDocumentText物件。 FirebaseVisionDocumentText物件包含影像中識別的全文以及反映已識別文件結構的物件層次結構：
-
FirebaseVisionDocumentText.Block -
FirebaseVisionDocumentText.Paragraph -
FirebaseVisionDocumentText.Word -
FirebaseVisionDocumentText.Symbol
對於每個Block 、 Paragraph 、 Word和Symbol對象，您可以獲得該區域中識別的文字以及該區域的邊界座標。
例如：
Kotlin+KTX
val resultText = result.text
for (block in result.blocks) {
val blockText = block.text
val blockConfidence = block.confidence
val blockRecognizedLanguages = block.recognizedLanguages
val blockFrame = block.boundingBox
for (paragraph in block.paragraphs) {
val paragraphText = paragraph.text
val paragraphConfidence = paragraph.confidence
val paragraphRecognizedLanguages = paragraph.recognizedLanguages
val paragraphFrame = paragraph.boundingBox
for (word in paragraph.words) {
val wordText = word.text
val wordConfidence = word.confidence
val wordRecognizedLanguages = word.recognizedLanguages
val wordFrame = word.boundingBox
for (symbol in word.symbols) {
val symbolText = symbol.text
val symbolConfidence = symbol.confidence
val symbolRecognizedLanguages = symbol.recognizedLanguages
val symbolFrame = symbol.boundingBox
}
}
}
}Java
String resultText = result.getText();
for (FirebaseVisionDocumentText.Block block: result.getBlocks()) {
String blockText = block.getText();
Float blockConfidence = block.getConfidence();
List<RecognizedLanguage> blockRecognizedLanguages = block.getRecognizedLanguages();
Rect blockFrame = block.getBoundingBox();
for (FirebaseVisionDocumentText.Paragraph paragraph: block.getParagraphs()) {
String paragraphText = paragraph.getText();
Float paragraphConfidence = paragraph.getConfidence();
List<RecognizedLanguage> paragraphRecognizedLanguages = paragraph.getRecognizedLanguages();
Rect paragraphFrame = paragraph.getBoundingBox();
for (FirebaseVisionDocumentText.Word word: paragraph.getWords()) {
String wordText = word.getText();
Float wordConfidence = word.getConfidence();
List<RecognizedLanguage> wordRecognizedLanguages = word.getRecognizedLanguages();
Rect wordFrame = word.getBoundingBox();
for (FirebaseVisionDocumentText.Symbol symbol: word.getSymbols()) {
String symbolText = symbol.getText();
Float symbolConfidence = symbol.getConfidence();
List<RecognizedLanguage> symbolRecognizedLanguages = symbol.getRecognizedLanguages();
Rect symbolFrame = symbol.getBoundingBox();
}
}
}
}下一步
- 在將使用雲端 API 的應用程式部署到生產環境之前，您應該採取一些額外的步驟來防止和減輕未經授權的 API 存取的影響。