在 Android 上使用 Firebase ML 來辨識圖片中的文字

您可以使用 Firebase ML 辨識圖片中的文字。Firebase ML 提供一般用途 API，可用於辨識圖片中的文字，例如路標上的文字，以及經過最佳化調整的 API，可用於辨識文件中的文字。

事前準備

  1. 如果您尚未將 Firebase 新增至 Android 專案，請新增 Firebase。
  2. 在模組 (應用程式層級) Gradle 檔案 (通常是 <project>/<app-module>/build.gradle.kts 或 <project>/<app-module>/build.gradle) 中，加入 Android 版 Firebase ML Vision 程式庫的依附元件。建議您使用 Firebase Android BoM 來控管程式庫版本。
    dependencies {
    // Import the BoM for the Firebase platform
    implementation(platform("com.google.firebase:firebase-bom:33.7.0"))

    // 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'
}

    只要使用 Firebase Android BoM，應用程式就會一律使用相容的 Firebase Android 程式庫版本。

    (替代做法)  不使用 BoM 新增 Firebase 程式庫依附元件

    如果您選擇不使用 Firebase BoM，則必須在依附元件行中指定每個 Firebase 程式庫版本。

    請注意，如果您在應用程式中使用多個 Firebase 程式庫，強烈建議您使用 BoM 來管理程式庫版本，確保所有版本皆相容。

    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'
}
         想尋找 Kotlin 專屬的程式庫模組嗎？自 2023 年 10 月 (Firebase BoM 32.5.0)起，Kotlin 和 Java 開發人員都可以依附主要程式庫模組 (詳情請參閱這項計畫的常見問題)。

  3. 如果您尚未為專案啟用雲端 API，請立即啟用：

    1. 開啟 Firebase 控制台的 Firebase ML API 頁面。

    2. 如果您尚未將專案升級至 Blaze 定價方案，請按一下「Upgrade」進行升級 (只有在專案未採用 Blaze 方案時，系統才會提示您升級)。

      只有 Blaze 級別專案可以使用雲端 API。

    3. 如果您尚未啟用雲端 API，請按一下「啟用雲端 API」。

您現在可以開始辨識圖片中的文字。

輸入圖片規範

  • 如要讓 Firebase ML 準確辨識文字，輸入圖片必須包含由足夠像素資料代表的文字。以拉丁文為例，每個字元應至少為 16 x 16 像素。若是中文、日文和韓文，每個字元應為 24x24 像素。對於所有語言而言，字元大小超過 24x24 像素通常不會帶來任何準確度優勢。

    舉例來說，如果要掃描名片，640x480 的圖片可能會佔用整個圖片的寬度，如要掃描以 A4 大小紙張列印的文件，可能需要 720 x 1280 像素的圖片。

  • 若圖片對焦不佳，文字辨識準確度可能會降低。如果您無法取得可接受的結果，請嘗試要求使用者重新拍攝圖片。

辨識圖片中的文字

如要辨識圖片中的文字，請按照下方說明執行文字辨識器。

1. 執行文字辨識器

如要辨識圖片中的文字，請從裝置上的 Bitmap、media.Image、ByteBuffer、位元組陣列或檔案建立 FirebaseVisionImage 物件。接著，將 FirebaseVisionImage 物件傳遞至 FirebaseVisionTextRecognizer 的 processImage 方法。

  1. 從圖片建立 FirebaseVisionImage 物件。

    • 如要從 media.Image 物件建立 FirebaseVisionImage 物件 (例如從裝置相機擷取圖片時)，請將 media.Image 物件和圖片的旋轉角度傳遞至 FirebaseVisionImage.fromMediaImage()。

      如果您使用 CameraX 程式庫，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，將旋轉值轉換為 Firebase ML 的 ROTATION_ 常數：

      Kotlin

      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
        // ...
    }
}

      如果您未使用可提供圖片旋轉角度的相機程式庫，可以根據裝置旋轉角度和裝置中相機感應器的方向計算：

      Kotlin

      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
}
      VisionImage.kt

      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;
}
      VisionImage.java

      接著，將 media.Image 物件和旋轉值傳遞至 FirebaseVisionImage.fromMediaImage()：

      Kotlin

      val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
      VisionImage.kt

      Java

      FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
      VisionImage.java
    • 如要從檔案 URI 建立 FirebaseVisionImage 物件，請將應用程式背景資訊和檔案 URI 傳遞至 FirebaseVisionImage.fromFilePath()。這在您使用 ACTION_GET_CONTENT 意圖，提示使用者從相片庫應用程式中選取圖片時，非常實用。

      Kotlin

      val image: FirebaseVisionImage
try {
    image = FirebaseVisionImage.fromFilePath(context, uri)
} catch (e: IOException) {
    e.printStackTrace()
}
      VisionImage.kt

      Java

      FirebaseVisionImage image;
try {
    image = FirebaseVisionImage.fromFilePath(context, uri);
} catch (IOException e) {
    e.printStackTrace();
}
      VisionImage.java
    • 如要從 ByteBuffer 或位元組陣列建立 FirebaseVisionImage 物件，請先計算圖片旋轉角度，如上文所述的 media.Image 輸入資料。

      接著，請建立 FirebaseVisionImageMetadata 物件，其中包含圖片的高度、寬度、顏色編碼格式和旋轉角度：

      Kotlin

      val metadata = FirebaseVisionImageMetadata.Builder()
    .setWidth(480) // 480x360 is typically sufficient for
    .setHeight(360) // image recognition
    .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21)
    .setRotation(rotation)
    .build()
      VisionImage.kt

      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();
      VisionImage.java

      使用緩衝區或陣列和中繼資料物件，建立 FirebaseVisionImage 物件：

      Kotlin

      val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata)
// Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
      VisionImage.kt

      Java

      FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata);
// Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
      VisionImage.java
    • 如要從 Bitmap 物件建立 FirebaseVisionImage 物件，請按照下列步驟操作：

      Kotlin

      val image = FirebaseVisionImage.fromBitmap(bitmap)
      VisionImage.kt

      Java

      FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
      VisionImage.java
      Bitmap 物件所代表的圖片必須是直立的，不需要額外旋轉。

  2. 取得 FirebaseVisionTextRecognizer 的例項。

    Kotlin 

    val detector = FirebaseVision.getInstance().cloudTextRecognizer
// Or, to change the default settings:
// val detector = FirebaseVision.getInstance().getCloudTextRecognizer(options)
    TextRecognitionActivity.kt
     
    // 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()
    TextRecognitionActivity.kt

    Java 

    FirebaseVisionTextRecognizer detector = FirebaseVision.getInstance()
        .getCloudTextRecognizer();
// Or, to change the default settings:
//   FirebaseVisionTextRecognizer detector = FirebaseVision.getInstance()
//          .getCloudTextRecognizer(options);
    TextRecognitionActivity.java
     
    // 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();
    TextRecognitionActivity.java

  3. 最後，將圖片傳遞至 processImage 方法：

    Kotlin 

    val result = detector.processImage(image)
    .addOnSuccessListener { firebaseVisionText ->
        // Task completed successfully
        // ...
    }
    .addOnFailureListener { e ->
        // Task failed with an exception
        // ...
    }
    TextRecognitionActivity.kt

    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
                                // ...
                            }
                        });
    TextRecognitionActivity.java

2. 從已辨識的文字區塊中擷取文字

如果文字辨識作業成功，系統會將 FirebaseVisionText 物件傳遞至成功事件監聽器。FirebaseVisionText 物件包含圖片中辨識到的完整文字，以及零個或多個 TextBlock 物件。

每個 TextBlock 都代表一個矩形文字區塊，其中可能包含零個或多個 Line 物件。每個 Line 物件都包含零個或多個 Element 物件，代表字詞和類似字詞的實體 (日期、數字等)。

您可以為每個 TextBlock、Line 和 Element 物件取得在該區域中辨識到的文字，以及該區域的邊界座標。

例如：

Kotlin 

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
        }
    }
}
TextRecognitionActivity.kt

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();
        }
    }
}
TextRecognitionActivity.java

後續步驟

辨識文件圖片中的文字

如要辨識文件中的文字，請按照下文所述設定及執行文件文字辨識器。

下文所述的文件文字辨識 API 提供的介面，旨在讓您更方便處理文件圖片。不過，如果您偏好 FirebaseVisionTextRecognizer API 提供的介面，可以改用該介面掃描文件，方法是設定雲端文字辨識器，以使用密集文字模型。

如要使用文件文字辨識 API，請按照下列步驟操作：

1. 執行文字辨識器

如要辨識圖片中的文字，請使用 Bitmap、media.Image、ByteBuffer、位元組陣列或裝置上的檔案，建立 FirebaseVisionImage 物件。接著，將 FirebaseVisionImage 物件傳遞至 FirebaseVisionDocumentTextRecognizer 的 processImage 方法。

  1. 從圖片建立 FirebaseVisionImage 物件。

    • 如要從 media.Image 物件建立 FirebaseVisionImage 物件 (例如從裝置相機擷取圖片時)，請將 media.Image 物件和圖片的旋轉角度傳遞至 FirebaseVisionImage.fromMediaImage()。

      如果您使用 CameraX 程式庫，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，將旋轉值轉換為 Firebase ML 的 ROTATION_ 常數：

      Kotlin

      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
        // ...
    }
}

      如果您未使用可提供圖片旋轉角度的相機程式庫，可以根據裝置旋轉角度和裝置中相機感應器的方向計算：

      Kotlin

      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
}
      VisionImage.kt

      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;
}
      VisionImage.java

      接著，將 media.Image 物件和旋轉值傳遞至 FirebaseVisionImage.fromMediaImage()：

      Kotlin

      val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
      VisionImage.kt

      Java

      FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
      VisionImage.java
    • 如要從檔案 URI 建立 FirebaseVisionImage 物件，請將應用程式背景資訊和檔案 URI 傳遞至 FirebaseVisionImage.fromFilePath()。這在您使用 ACTION_GET_CONTENT 意圖，提示使用者從相片庫應用程式中選取圖片時，非常實用。

      Kotlin

      val image: FirebaseVisionImage
try {
    image = FirebaseVisionImage.fromFilePath(context, uri)
} catch (e: IOException) {
    e.printStackTrace()
}
      VisionImage.kt

      Java

      FirebaseVisionImage image;
try {
    image = FirebaseVisionImage.fromFilePath(context, uri);
} catch (IOException e) {
    e.printStackTrace();
}
      VisionImage.java
    • 如要從 ByteBuffer 或位元組陣列建立 FirebaseVisionImage 物件，請先計算圖片旋轉角度，如上文所述的 media.Image 輸入資料。

      接著，請建立 FirebaseVisionImageMetadata 物件，其中包含圖片的高度、寬度、顏色編碼格式和旋轉角度：

      Kotlin

      val metadata = FirebaseVisionImageMetadata.Builder()
    .setWidth(480) // 480x360 is typically sufficient for
    .setHeight(360) // image recognition
    .setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21)
    .setRotation(rotation)
    .build()
      VisionImage.kt

      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();
      VisionImage.java

      使用緩衝區或陣列和中繼資料物件，建立 FirebaseVisionImage 物件：

      Kotlin

      val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata)
// Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
      VisionImage.kt

      Java

      FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata);
// Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
      VisionImage.java
    • 如要從 Bitmap 物件建立 FirebaseVisionImage 物件，請按照下列步驟操作：

      Kotlin

      val image = FirebaseVisionImage.fromBitmap(bitmap)
      VisionImage.kt

      Java

      FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
      VisionImage.java
      Bitmap 物件所代表的圖片必須是直立的，不需要額外旋轉。

  2. 取得 FirebaseVisionDocumentTextRecognizer 的例項：

    Kotlin 

    val detector = FirebaseVision.getInstance()
    .cloudDocumentTextRecognizer
    TextRecognitionActivity.kt
     
    // 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)
    TextRecognitionActivity.kt

    Java 

    FirebaseVisionDocumentTextRecognizer detector = FirebaseVision.getInstance()
        .getCloudDocumentTextRecognizer();
    TextRecognitionActivity.java
     
    // 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);
    TextRecognitionActivity.java

  3. 最後，將圖片傳遞至 processImage 方法：

    Kotlin 

    detector.processImage(myImage)
    .addOnSuccessListener { firebaseVisionDocumentText ->
        // Task completed successfully
        // ...
    }
    .addOnFailureListener { e ->
        // Task failed with an exception
        // ...
    }
    TextRecognitionActivity.kt

    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
                // ...
            }
        });
    TextRecognitionActivity.java

2. 從已辨識的文字區塊中擷取文字

如果文字辨識作業成功，系統會傳回 FirebaseVisionDocumentText 物件。FirebaseVisionDocumentText 物件包含圖片中辨識到的完整文字，以及反映已辨識文件結構的物件階層：

您可以為每個 Block、Paragraph、Word 和 Symbol 物件取得在該區域中辨識到的文字，以及該區域的邊界座標。

例如：

Kotlin 

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
            }
        }
    }
}
TextRecognitionActivity.kt

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();
            }
        }
    }
}
TextRecognitionActivity.java

後續步驟