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update to v2 (beta)
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‎packages/learning_text_recognition/CHANGELOG.md

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## [0.0.4] - 23 November 2021
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* Update to MLKit Text Recognition v2 (Beta) to support Chinese, Devanagari, Japanese and Korean scripts
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## [0.0.3] - 23 November 2021
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* Update dependencies

‎packages/learning_text_recognition/README.md

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The easy way to use ML Kit for text recognition in Flutter.
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ML Kit's text recognition can recognize text in any Latin-based character set. They can also be used to automate data-entry tasks such as processing credit cards, receipts, and business cards.
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ML Kit's text recognition can recognize text in Latin, Chinese, Devanagari, Japanese and Korean scripts and a [wide range of languages](https://developers.google.com/ml-kit/vision/text-recognition/v2/languages). They can also be used to automate data-entry tasks such as processing credit cards, receipts, and business cards.
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<img src="https://github.com/salkuadrat/learning/raw/master/packages/learning_text_recognition/screenshot.jpg" alt="universe" width="280">
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## Getting Started
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Add dependency to your flutter project:
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```
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$ flutter pub add learning_text_recognition
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```
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or
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```yaml
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dependencies:
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learning_text_recognition: ^0.0.2
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```bash
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$ pub add learning_text_recognition
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```
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Then run `flutter pub get`.
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<!--
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## Configuration
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You can configure your app to automatically download the ML model to the device after your app is installed from the Play Store. To do so, add the following declaration to your app's AndroidManifest.xml file.
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<application ...>
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...
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<meta-data
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android:name="com.google.mlkit.vision.DEPENDENCIES"
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android:value="ocr" />
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<!-- To use multiple models: android:value="ocr,model2,model3" -->
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android:name="com.google.mlkit.vision.DEPENDENCIES"
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android:value="ocr" />
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</application>
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```
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If you do not enable install-time model downloads, the model will be downloaded the first time you run the text recognition process. Requests you make before the download has completed will produce no results.
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-->
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## Usage
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```
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```dart
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import 'package:learning_text_recognition/learning_text_recognition.dart';
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```
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After getting the `InputImage`, we can start doing text recognition by calling method `process` from an instance of `TextRecognition`.
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```dart
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// When using for Latin script
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TextRecognition textRecognition = TextRecognition();
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// or like this:
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TextRecognition textRecognition = TextRecognition(
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options: TextRecognitionOptions.Default
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);
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// When using for Chinese script
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TextRecognition textRecognition = TextRecognition(
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options: TextRecognitionOptions.Chinese
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);
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// When using for Devanagari script
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TextRecognition textRecognition = TextRecognition(
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options: TextRecognitionOptions.Devanagari
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);
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// When using for Japanese script
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TextRecognition textRecognition = TextRecognition(
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options: TextRecognitionOptions.Japanese
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);
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// When using for Korean script
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TextRecognition textRecognition = TextRecognition(
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options: TextRecognitionOptions.Korean
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);
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// Process text recognition...
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RecognizedText result = await textRecognition.process(image);
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```
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‎packages/learning_text_recognition/android/build.gradle

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dependencies {
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// MLKit Text Recognition
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implementation 'com.google.android.gms:play-services-mlkit-text-recognition:16.3.0'
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// implementation 'com.google.android.gms:play-services-mlkit-text-recognition:16.3.0'
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// To recognize Latin script
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implementation 'com.google.mlkit:text-recognition:16.0.0-beta1'
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// To recognize Chinese script
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implementation 'com.google.mlkit:text-recognition-chinese:16.0.0-beta1'
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// To recognize Devanagari script
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implementation 'com.google.mlkit:text-recognition-devanagari:16.0.0-beta1'
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// To recognize Japanese script
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implementation 'com.google.mlkit:text-recognition-japanese:16.0.0-beta1'
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// To recognize Korean script
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implementation 'com.google.mlkit:text-recognition-korean:16.0.0-beta1'
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}

‎packages/learning_text_recognition/android/src/main/java/com/salkuadrat/learning/learning_text_recognition/LearningTextRecognitionPlugin.java

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private void process(MethodCall call, Result result) {
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InputImage image = getInputImage(call, result);
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String options = call.argument("options");
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if (image != null) {
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textRecognition = new TextRecognition();
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textRecognition = new TextRecognition(options);
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textRecognition.process(image, result);
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}
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}

‎packages/learning_text_recognition/android/src/main/java/com/salkuadrat/learning/learning_text_recognition/TextRecognition.java

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import com.google.mlkit.vision.common.InputImage;
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import com.google.mlkit.vision.text.Text;
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import com.google.mlkit.vision.text.TextRecognizer;
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import com.google.mlkit.vision.text.TextRecognizerOptions;
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import com.google.mlkit.vision.text.latin.TextRecognizerOptions;
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import com.google.mlkit.vision.text.chinese.ChineseTextRecognizerOptions;
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import com.google.mlkit.vision.text.devanagari.DevanagariTextRecognizerOptions;
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import com.google.mlkit.vision.text.japanese.JapaneseTextRecognizerOptions;
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import com.google.mlkit.vision.text.korean.KoreanTextRecognizerOptions;
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import java.util.ArrayList;
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import java.util.HashMap;
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final TextRecognizer recognizer;
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public TextRecognition() {
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this.recognizer = com.google.mlkit.vision.text.TextRecognition.getClient(TextRecognizerOptions.DEFAULT_OPTIONS);
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public TextRecognition(String options) {
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if (options == "chinese") {
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this.recognizer = com.google.mlkit.vision.text.TextRecognition.getClient(new ChineseTextRecognizerOptions.Builder().build());
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} else if (options == "devanagari") {
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this.recognizer = com.google.mlkit.vision.text.TextRecognition.getClient(new DevanagariTextRecognizerOptions.Builder().build());
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} else if (options == "japanese") {
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this.recognizer = com.google.mlkit.vision.text.TextRecognition.getClient(new JapaneseTextRecognizerOptions.Builder().build());
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} else if (options == "korean") {
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this.recognizer = com.google.mlkit.vision.text.TextRecognition.getClient(new KoreanTextRecognizerOptions.Builder().build());
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} else {
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this.recognizer = com.google.mlkit.vision.text.TextRecognition.getClient(TextRecognizerOptions.DEFAULT_OPTIONS);
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}
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}
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public void process(@NonNull InputImage image, @NonNull final MethodChannel.Result result) {
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}
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return result;
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}
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public void dispose() {
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if (recognizer != null) {
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recognizer.close();

‎packages/learning_text_recognition/example/android/app/src/main/AndroidManifest.xml

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<meta-data
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android:name="flutterEmbedding"
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android:value="2" />
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<meta-data
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<!-- <meta-data
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android:name="com.google.mlkit.vision.DEPENDENCIES"
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android:value="ocr" />
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-->
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</application>
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</manifest>

‎packages/learning_text_recognition/example/lib/main.dart

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class _TextRecognitionPageState extends State<TextRecognitionPage> {
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TextRecognition? _textRecognition = TextRecognition();
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/* TextRecognition? _textRecognition = TextRecognition(
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options: TextRecognitionOptions.Japanese
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); */
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@override
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void dispose() {
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_textRecognition?.dispose();

‎packages/learning_text_recognition/example/pubspec.lock

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path: ".."
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relative: true
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source: path
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version: "0.0.3"
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version: "0.0.4"
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matcher:
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dependency: transitive
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description:

‎packages/learning_text_recognition/lib/learning_text_recognition.dart

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library learning_text_recognition;
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export 'src/options.dart';
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export 'src/text_recognition.dart';
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export 'src/text.dart';

‎packages/learning_text_recognition/lib/src/options.dart

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enum TextRecognitionOptions {
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Default,
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Chinese,
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Devanagari,
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Japanese,
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Korean,
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}
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String fromOptions(TextRecognitionOptions options) {
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switch (options) {
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case TextRecognitionOptions.Chinese:
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return 'chinese';
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case TextRecognitionOptions.Devanagari:
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return 'devanagari';
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case TextRecognitionOptions.Japanese:
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return 'japanese';
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case TextRecognitionOptions.Korean:
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return 'korean';
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default:
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return 'default';
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}
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}

‎packages/learning_text_recognition/lib/src/text_recognition.dart

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import 'package:flutter/services.dart';
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import 'package:learning_input_image/learning_input_image.dart';
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import 'options.dart';
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import 'text.dart';
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class TextRecognition {
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final MethodChannel channel = MethodChannel('LearningTextRecognition');
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final TextRecognitionOptions options;
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TextRecognition();
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TextRecognition({this.options = TextRecognitionOptions.Default});
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Future<RecognizedText?> process(InputImage image) async {
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try {
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final result = await channel
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.invokeMethod('process', <String, dynamic>{'image': image.json});
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final result = await channel.invokeMethod('process', <String, dynamic>{
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'image': image.json,
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'options': fromOptions(options),
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});
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if (result != null) {
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return RecognizedText.from(result);

‎packages/learning_text_recognition/pubspec.yaml

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name: learning_text_recognition
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description: The easy way to use ML Kit for text recognition in Flutter.
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version: 0.0.3
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version: 0.0.4
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# author: Salman S
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homepage: https://github.com/salkuadrat/learning/tree/master/packages/learning_text_recognition
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repository: https://github.com/salkuadrat/learning/tree/master/packages/learning_text_recognition

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