在 Android 上使用 Firebase ML 來辨識圖片中的文字

你可以使用 Firebase ML 辨識圖片中的文字。Firebase ML 的 適用於辨識圖片中的文字 標誌文字，以及最佳化的 API，可辨識 文件。

事前準備

  1. 如果還沒試過 將 Firebase 新增至您的 Android 專案。
  2. 在模組 (應用程式層級) Gradle 檔案中 (通常為 <project>/<app-module>/build.gradle.kts 或 <project>/<app-module>/build.gradle)、 新增 Android 版 Firebase ML Vision 程式庫的依附元件。建議您使用 Firebase Android BoM 管理程式庫版本管理 
    dependencies {
    // Import the BoM for the Firebase platform
    implementation(platform("com.google.firebase:firebase-bom:33.1.2"))

    // 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'
}

    使用 Firebase Android BoM 應用程式一律會使用相容的 Firebase Android 程式庫版本。

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

    如果選擇不使用 Firebase BoM，則須指定各個 Firebase 程式庫版本 都屬於依附元件行

    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 建議使用 BoM 管理程式庫版本 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'
}
    在尋找 Kotlin 專用的程式庫模組嗎？距離開始還有 2023 年 10 月 (Firebase BoM 32.5.0)，Kotlin 和 Java 開發人員都能 依附於主要程式庫模組 (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式 API，請先啟用 現在：

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

    2. 如果您尚未將專案升級至 Blaze 定價方案，請按一下 如要這麼做，請升級。(只有在您的 專案並未採用 Blaze 方案)。

      只有 Blaze 層級的專案可以使用以雲端為基礎的 API。

    3. 如果尚未啟用雲端式 API，請按一下「Enable Cloud-based API」(啟用雲端式 API) API

現在可以開始辨識圖片中的文字。

輸入圖片規範

  • 為了讓 Firebase ML 準確辨識文字，輸入圖片必須包含 以充足的像素資料表示的文字最適合拉丁字母 每個字元至少要有 16x16 像素中文 日文和韓文文字 字元應為 24x24 像素所有語言通常沒有 對字元大於 24x24 像素的特性來說，準確性的優勢在於。

    舉例來說，640x480 的圖片適合掃描名片 圖片會佔滿圖片的整個寬度如何掃描列印的文件 則建議使用 720x1280 像素的圖片。

  • 圖片焦點不佳可能會降低文字辨識的準確度。如果您不 請嘗試重新擷取圖片。

辨識圖片中的文字

如要辨識圖片中的文字，請按照說明執行文字辨識工具 。

1. 執行文字辨識工具

如要辨識圖片中的文字，請建立 FirebaseVisionImage 物件 從 Bitmap、media.Image、ByteBuffer、位元組陣列或 裝置。然後，將 FirebaseVisionImage 物件傳遞至 FirebaseVisionTextRecognizer 的 processImage 方法。

  1. 使用圖片建立 FirebaseVisionImage 物件。

    • 如何透過FirebaseVisionImage media.Image 物件，例如從 裝置的相機，請傳遞 media.Image 物件和圖片的 旋轉至 FirebaseVisionImage.fromMediaImage()。

      如果您使用 CameraX 程式庫、OnImageCapturedListener 和 ImageAnalysis.Analyzer 類別會計算旋轉值 因此您只需將輪替轉換為 Firebase ML 的 呼叫前 ROTATION_ 常數 FirebaseVisionImage.fromMediaImage()：

      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
        // ...
    }
}

      如果您沒有使用相機程式庫來提供圖像旋轉角度， 可根據裝置旋轉角度和相機方向計算 感應器：

      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
}
      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

      然後，請傳遞 media.Image 物件和 將旋轉值轉換為 FirebaseVisionImage.fromMediaImage()：

      Kotlin+KTX

      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，請傳遞 應用程式環境和檔案 URI FirebaseVisionImage.fromFilePath()。如果您要 使用 ACTION_GET_CONTENT 意圖提示使用者選取 取自圖片庫應用程式中的圖片。

      Kotlin+KTX

      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
    • 如何透過FirebaseVisionImage ByteBuffer 或位元組陣列，請先計算圖片 旋轉 (方法如上所述) media.Image 輸入欄位。

      接著建立 FirebaseVisionImageMetadata 物件 包含圖片的高度、寬度、色彩編碼格式 和輪替金鑰

      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()
      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 FirebaseVisionImage 物件：

      Kotlin+KTX

      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
    • 如何透過FirebaseVisionImage Bitmap 物件：

      Kotlin+KTX

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

      Java

      FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
      VisionImage.java
      Bitmap 物件代表的圖片必須 保持直立，不用另外旋轉。

  2. 取得 FirebaseVisionTextRecognizer 的執行個體。

    Kotlin+KTX 

    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+KTX 

    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 接聽程式。FirebaseVisionText 物件包含系統辨識到的文字 映像檔和零或多個 TextBlock 物件

每個 TextBlock 都代表文字區塊，其中包含零或 其他 Line 物件。每個 Line 物件都包含零個或多個物件 Element 物件，代表字詞和類似文字 實體 (日期、數字等)。

對於每個 TextBlock、Line 和 Element 物件，您可以取得文字 辨識其特徵的位置和邊界座標

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
        }
    }
}
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，請設定並執行 與文件文字辨識工具搭配使用

以下說明文件文字辨識 API 提供的介面 是為了方便處理文件圖片。不過 如果您偏好 FirebaseVisionTextRecognizer API 提供的介面 您可以改用 BigQuery 掃描文件 辨識器來使用密集文字模型。

如何使用文件文字辨識 API：

1. 執行文字辨識工具

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

  1. 使用圖片建立 FirebaseVisionImage 物件。

    • 如何透過FirebaseVisionImage media.Image 物件，例如從 裝置的相機，請傳遞 media.Image 物件和圖片的 旋轉至 FirebaseVisionImage.fromMediaImage()。

      如果您使用 CameraX 程式庫、OnImageCapturedListener 和 ImageAnalysis.Analyzer 類別會計算旋轉值 因此您只需將輪替轉換為 Firebase ML 的 呼叫前 ROTATION_ 常數 FirebaseVisionImage.fromMediaImage()：

      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
        // ...
    }
}

      如果您沒有使用相機程式庫來提供圖像旋轉角度， 可根據裝置旋轉角度和相機方向計算 感應器：

      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
}
      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

      然後，請傳遞 media.Image 物件和 將旋轉值轉換為 FirebaseVisionImage.fromMediaImage()：

      Kotlin+KTX

      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，請傳遞 應用程式環境和檔案 URI FirebaseVisionImage.fromFilePath()。如果您要 使用 ACTION_GET_CONTENT 意圖提示使用者選取 取自圖片庫應用程式中的圖片。

      Kotlin+KTX

      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
    • 如何透過FirebaseVisionImage ByteBuffer 或位元組陣列，請先計算圖片 旋轉 (方法如上所述) media.Image 輸入欄位。

      接著建立 FirebaseVisionImageMetadata 物件 包含圖片的高度、寬度、色彩編碼格式 和輪替金鑰

      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()
      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 FirebaseVisionImage 物件：

      Kotlin+KTX

      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
    • 如何透過FirebaseVisionImage Bitmap 物件：

      Kotlin+KTX

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

      Java

      FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
      VisionImage.java
      Bitmap 物件代表的圖片必須 保持直立，不用另外旋轉。

  2. 取得 FirebaseVisionDocumentTextRecognizer：

    Kotlin+KTX 

    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+KTX 

    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擷取文字

如果文字辨識作業成功，系統會傳回 FirebaseVisionDocumentText 物件。A 罩杯 FirebaseVisionDocumentText 物件包含可在 圖片及物件階層，反映可識別的 文件：

對於每個 Block、Paragraph、Word 和 Symbol 物件，您可以取得 可在區域辨識的文字和區域的邊界座標。

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
            }
        }
    }
}
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