你可以使用 ML Kit 辨識圖片中的文字。ML Kit 提供適用於辨識圖片文字 (例如路標文字) 的一般用途 API,以及用於辨識文件文字最佳化的 API。一般用途的 API 同時具備裝置和雲端模型。 文件文字辨識功能僅適用於雲端式模型。請參閱總覽,瞭解雲端和裝置端模型的比較。
事前準備
- 如果您尚未將 Firebase 新增至 Android 專案,請先完成這項操作。
- 將 ML Kit Android 程式庫的依附元件新增至模組 (應用程式層級) Gradle 檔案 (通常是
app/build.gradle
):apply plugin: 'com.android.application' apply plugin: 'com.google.gms.google-services' dependencies { // ... implementation 'com.google.firebase:firebase-ml-vision:24.0.3' }
-
選用,但建議使用:如果您使用裝置端 API,請將應用程式設為在從 Play 商店安裝應用程式後,自動將機器學習模型下載至裝置。
如要這麼做,請在應用程式的
AndroidManifest.xml
檔案中新增以下宣告:<application ...> ... <meta-data android:name="com.google.firebase.ml.vision.DEPENDENCIES" android:value="ocr" /> <!-- To use multiple models: android:value="ocr,model2,model3" --> </application>
如未啟用安裝期間模型下載功能,系統會在您首次執行裝置端偵測工具時下載模型。下載完成前提出的要求不會產生任何結果。 -
如要使用以雲端為基礎的模型,且您尚未為專案啟用雲端式 API,請立即啟用:
- 開啟 Firebase 控制台的 ML Kit API 頁面。
-
如果您尚未將專案升級至 Blaze 定價方案,按一下「升級」即可進行升級 (只有在專案未採用 Blaze 方案時,系統才會提示您升級)。
只有 Blaze 層級的專案可以使用以雲端為基礎的 API。
- 如果雲端型 API 尚未啟用,請點選「啟用雲端式 API」。
如果只想使用裝置端模型,可以略過這個步驟。
現在可以開始辨識圖片中的文字。
輸入圖片規範
-
為了讓 ML Kit 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。理想情況下,拉丁文字的每個字元至少要有 16x16 像素。如果是中文、日文和韓文文字 (只有雲端式 API 支援),則每個字元應為 24x24 像素。對於所有語言來說,大於 24x24 像素的字元通常沒有什麼助益。
舉例來說,640x480 的圖片可能適合掃描佔圖片整個寬度的名片,如要掃描印在正大尺寸紙上的文件,可能需要使用 720 x 1280 像素的圖片。
-
圖片焦點不佳可能會降低文字辨識的準確度。如果您仍未取得可接受的結果,請嘗試要求使用者重新拍攝圖片。
-
如果您在即時應用程式中辨識文字,建議您也考量輸入圖片的整體尺寸。系統處理較小的圖片可加快處理速度,因此為了縮短延遲時間,建議你以較低的解析度擷取圖片 (請注意上述準確率規定),並確保文字盡可能佔用圖片。另請參閱「即時效能改善提示」。
辨識圖片中的文字
如要使用裝置或雲端式模型辨識圖片中的文字,請按照下列步驟操作,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,請透過Bitmap
、media.Image
、ByteBuffer
、位元組陣列或裝置上的檔案建立 FirebaseVisionImage
物件。然後將 FirebaseVisionImage
物件傳遞至 FirebaseVisionTextRecognizer
的 processImage
方法。
使用圖片建立
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),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,因此只要在呼叫FirebaseVisionImage.fromMediaImage()
之前,將旋轉角度轉換為 ML Kit 的ROTATION_
常數即可: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 Kit Vision API // ... } }
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 Kit Vision API // ... } } }
如果您使用的相機程式庫不提供圖像旋轉功能,您可依據裝置旋轉情形和裝置相機感應器方向計算曝光:
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; }
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 }
然後將
media.Image
物件和旋轉值傳遞至FirebaseVisionImage.fromMediaImage()
:Java
FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
Kotlin+KTX
val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
- 如要從檔案 URI 建立
FirebaseVisionImage
物件,請將應用程式結構定義和檔案 URI 傳遞至FirebaseVisionImage.fromFilePath()
。使用ACTION_GET_CONTENT
意圖提示使用者從圖片庫應用程式中選取圖片時,這項功能就很實用。Java
FirebaseVisionImage image; try { image = FirebaseVisionImage.fromFilePath(context, uri); } catch (IOException e) { e.printStackTrace(); }
Kotlin+KTX
val image: FirebaseVisionImage try { image = FirebaseVisionImage.fromFilePath(context, uri) } catch (e: IOException) { e.printStackTrace() }
- 如要從
ByteBuffer
或位元組陣列建立FirebaseVisionImage
物件,請先按照上述的media.Image
輸入方式計算圖片旋轉角度。接著,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: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();
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()
使用緩衝區或陣列和中繼資料物件建立
FirebaseVisionImage
物件:Java
FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata); // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
Kotlin+KTX
val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata) // Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
- 如要從
Bitmap
物件建立FirebaseVisionImage
物件,請按照下列步驟操作:Java
FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
Kotlin+KTX
val image = FirebaseVisionImage.fromBitmap(bitmap)
Bitmap
物件代表的圖片必須直立,無需額外旋轉。
-
取得
FirebaseVisionTextRecognizer
的執行個體。如何使用裝置端模型:
Java
FirebaseVisionTextRecognizer detector = FirebaseVision.getInstance() .getOnDeviceTextRecognizer();
Kotlin+KTX
val detector = FirebaseVision.getInstance() .onDeviceTextRecognizer
如要使用雲端模型:
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();
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()
最後,將圖片傳遞至
processImage
方法: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 // ... } });
Kotlin+KTX
val result = detector.processImage(image) .addOnSuccessListener { firebaseVisionText -> // Task completed successfully // ... } .addOnFailureListener { 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,FirebaseVisionText
物件會傳遞給成功事件監聽器。FirebaseVisionText
物件包含圖片中可辨識的完整文字,以及零或多個 TextBlock
物件。
每個 TextBlock
都代表矩形文字區塊,其中包含零或多個 Line
物件。每個 Line
物件都含有零或多個 Element
物件,這些物件代表字詞和類似文字的實體 (日期、數字等)。
對於每個 TextBlock
、Line
和 Element
物件,您可以取得在區域辨識的文字和該區域的邊界座標。
例如:
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(); } } }
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 } } }
即時效能改善訣竅
如要在即時應用程式中,使用裝置端模型辨識文字,請按照下列指南操作,以便達到最佳的影格速率:
- 限制對文字辨識工具的呼叫。在文字辨識工具執行期間,如果有新的影片影格可供使用,請捨棄該影格。
- 如果您使用文字辨識工具的輸出內容來疊加輸入圖像上的圖像,請先從 ML Kit 取得結果,然後透過一個步驟算繪圖像和疊加層。這樣一來,每個輸入影格就只會算繪到顯示介面一次。
-
如果你使用 Camera2 API,請擷取
ImageFormat.YUV_420_888
格式的圖片。如果您使用舊版 Camera API,請拍攝
ImageFormat.NV21
格式的圖片。 - 建議以較低的解析度拍攝圖片。不過,也請注意這個 API 的圖片尺寸規定。
後續步驟
- 在部署至使用 Cloud API 的正式版應用程式之前,建議先採取一些額外步驟,預防及降低未經授權 API 存取所造成的影響。
辨識文件圖片中的文字
如要辨識文件的文字,請按照下列說明設定並執行雲端文件文字辨識工具。
如下所述文件文字辨識 API 所提供的介面,讓您更輕鬆地處理文件的圖片。不過,如果您偏好 FirebaseVisionTextRecognizer
API 提供的介面,只要設定雲端文字辨識工具來使用密集文字模型,即可改用該介面掃描文件。
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Bitmap
、media.Image
、ByteBuffer
、位元組陣列或裝置上的檔案建立 FirebaseVisionImage
物件。然後將 FirebaseVisionImage
物件傳遞至 FirebaseVisionDocumentTextRecognizer
的 processImage
方法。
使用圖片建立
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),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,因此只要在呼叫FirebaseVisionImage.fromMediaImage()
之前,將旋轉角度轉換為 ML Kit 的ROTATION_
常數即可: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 Kit Vision API // ... } }
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 Kit Vision API // ... } } }
如果您使用的相機程式庫不提供圖像旋轉功能,您可依據裝置旋轉情形和裝置相機感應器方向計算曝光:
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; }
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 }
然後將
media.Image
物件和旋轉值傳遞至FirebaseVisionImage.fromMediaImage()
:Java
FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
Kotlin+KTX
val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
- 如要從檔案 URI 建立
FirebaseVisionImage
物件,請將應用程式結構定義和檔案 URI 傳遞至FirebaseVisionImage.fromFilePath()
。使用ACTION_GET_CONTENT
意圖提示使用者從圖片庫應用程式中選取圖片時,這項功能就很實用。Java
FirebaseVisionImage image; try { image = FirebaseVisionImage.fromFilePath(context, uri); } catch (IOException e) { e.printStackTrace(); }
Kotlin+KTX
val image: FirebaseVisionImage try { image = FirebaseVisionImage.fromFilePath(context, uri) } catch (e: IOException) { e.printStackTrace() }
- 如要從
ByteBuffer
或位元組陣列建立FirebaseVisionImage
物件,請先按照上述的media.Image
輸入方式計算圖片旋轉角度。接著,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: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();
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()
使用緩衝區或陣列和中繼資料物件建立
FirebaseVisionImage
物件:Java
FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata); // Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
Kotlin+KTX
val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata) // Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
- 如要從
Bitmap
物件建立FirebaseVisionImage
物件,請按照下列步驟操作:Java
FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
Kotlin+KTX
val image = FirebaseVisionImage.fromBitmap(bitmap)
Bitmap
物件代表的圖片必須直立,無需額外旋轉。
-
取得
FirebaseVisionDocumentTextRecognizer
的執行個體: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);
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)
最後,將圖片傳遞至
processImage
方法: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 // ... } });
Kotlin+KTX
detector.processImage(myImage) .addOnSuccessListener { firebaseVisionDocumentText -> // Task completed successfully // ... } .addOnFailureListener { 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,會傳回 FirebaseVisionDocumentText
物件。FirebaseVisionDocumentText
物件包含圖片中可辨識的完整文字,以及反映所辨識文件結構的物件階層:
FirebaseVisionDocumentText.Block
FirebaseVisionDocumentText.Paragraph
FirebaseVisionDocumentText.Word
FirebaseVisionDocumentText.Symbol
對於每個 Block
、Paragraph
、Word
和 Symbol
物件,您可以取得區域中辨識的文字和該區域的定界座標。
例如:
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(); } } } }
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 } } } }
後續步驟
- 在部署至使用 Cloud API 的正式版應用程式之前,建議先採取一些額外步驟,預防及降低未經授權 API 存取所造成的影響。