您可以使用 Firebase ML 辨識圖片中的文字。Firebase ML擁有 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。
,瞭解如何調查及移除這項存取權。事前準備
- 如果還沒試過 將 Firebase 新增至您的 Android 專案。
-
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
)、 為 Android 專用的 Firebase ML Vision 程式庫新增依附元件。建議您使用 Firebase Android BoM敬上 管理程式庫版本管理dependencies { // Import the BoM for the Firebase platform implementation(platform("com.google.firebase:firebase-bom:33.2.0")) // 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,請指定各個 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,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' }
-
如果您尚未為專案啟用雲端式 API,請先啟用 現在:
- 開啟 Firebase ML Firebase 控制台的 API 頁面。
-
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。
- 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。
使用圖片建立
FirebaseVisionImage
物件。-
要使用
FirebaseVisionImage
物件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,您只需將旋轉角度轉換為 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 }
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,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)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
- 如要從檔案 URI 建立
FirebaseVisionImage
物件,請傳遞 應用程式環境和檔案 URIFirebaseVisionImage.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() }
Java
FirebaseVisionImage image; try { image = FirebaseVisionImage.fromFilePath(context, uri); } catch (IOException e) { e.printStackTrace(); }
- 要使用
FirebaseVisionImage
物件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()
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)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata); // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
- 要使用
FirebaseVisionImage
物件Bitmap
物件:Kotlin+KTX
val image = FirebaseVisionImage.fromBitmap(bitmap)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
Bitmap
物件代表的圖片必須 保持直立,不用另外旋轉。
-
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)
// 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 // ... }
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
辨識其特徵的意義和區域的邊界座標。
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(); } } }
後續步驟
- 部署至使用 Cloud API 的正式版應用程式之前,您應先完成 防範及減少 未經授權 API 存取的影響
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。
使用圖片建立
FirebaseVisionImage
物件。-
要使用
FirebaseVisionImage
物件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,您只需將旋轉角度轉換為 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 }
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,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)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
- 如要從檔案 URI 建立
FirebaseVisionImage
物件,請傳遞 應用程式環境和檔案 URIFirebaseVisionImage.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() }
Java
FirebaseVisionImage image; try { image = FirebaseVisionImage.fromFilePath(context, uri); } catch (IOException e) { e.printStackTrace(); }
- 要使用
FirebaseVisionImage
物件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()
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)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata); // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
- 要使用
FirebaseVisionImage
物件Bitmap
物件:Kotlin+KTX
val image = FirebaseVisionImage.fromBitmap(bitmap)
Java
FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
Bitmap
物件代表的圖片必須 保持直立,不用另外旋轉。
-
取得
FirebaseVisionDocumentTextRecognizer
: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,將圖片傳遞至
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:
FirebaseVisionDocumentText.Block
FirebaseVisionDocumentText.Paragraph
FirebaseVisionDocumentText.Word
FirebaseVisionDocumentText.Symbol
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 } } } }
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(); } } } }
後續步驟
- 部署至使用 Cloud API 的正式版應用程式之前,您應先完成 防範及減少 未經授權 API 存取的影響