VerticalOCRModule

TypeScript API implementation of the useVerticalOCR hook.

Reference

import {
  DETECTOR_CRAFT_1280,
  DETECTOR_CRAFT_320,
  RECOGNIZER_EN_CRNN_512,
  RECOGNIZER_EN_CRNN_64,
  useVerticalOCR,
} from 'react-native-executorch';

const imageUri = 'path/to/image.png';

// Loading the model
await VerticalOCRModule.load({
  detectorSources: {
    detectorLarge: DETECTOR_CRAFT_1280,
    detectorNarrow: DETECTOR_CRAFT_320,
  },
  recognizerSources: {
    recognizerLarge: RECOGNIZER_EN_CRNN_512,
    recognizerSmall: RECOGNIZER_EN_CRNN_64,
  },
  language: 'en',
  independentCharacters: true,
});

// Running the model
const ocrDetections = await VerticalOCRModule.forward(imageUri);

Methods

Method	Type	Description
load	(detectorSources: DetectorSources, recognizerSources: RecognizerSources, language: OCRLanguage independentCharacters: boolean): Promise<void>	Loads detectors and recognizers, which sources are represented by DetectorSources and RecognizerSources.
forward	(input: string): Promise<OCRDetections[]>	Executes the model's forward pass, where input can be a fetchable resource or a Base64-encoded string.
onDownloadProgress	(callback: (downloadProgress: number) => void): any	Subscribe to the download progress event.

Type definitions

interface DetectorSources {
  detectorLarge: string | number;
  detectorNarrow: string | number;
}

interface RecognizerSources {
  recognizerLarge: string | number;
  recognizerSmall: string | number;
}

type OCRLanguage =
  | 'abq'
  | 'ady'
  | 'af'
  | 'ava'
  | 'az'
  | 'be'
  | 'bg'
  | 'bs'
  | 'chSim'
  | 'che'
  | 'cs'
  | 'cy'
  | 'da'
  | 'dar'
  | 'de'
  | 'en'
  | 'es'
  | 'et'
  | 'fr'
  | 'ga'
  | 'hr'
  | 'hu'
  | 'id'
  | 'inh'
  | 'ic'
  | 'it'
  | 'ja'
  | 'kbd'
  | 'kn'
  | 'ko'
  | 'ku'
  | 'la'
  | 'lbe'
  | 'lez'
  | 'lt'
  | 'lv'
  | 'mi'
  | 'mn'
  | 'ms'
  | 'mt'
  | 'nl'
  | 'no'
  | 'oc'
  | 'pi'
  | 'pl'
  | 'pt'
  | 'ro'
  | 'ru'
  | 'rsCyrillic'
  | 'rsLatin'
  | 'sk'
  | 'sl'
  | 'sq'
  | 'sv'
  | 'sw'
  | 'tab'
  | 'te'
  | 'th'
  | 'tjk'
  | 'tl'
  | 'tr'
  | 'uk'
  | 'uz'
  | 'vi';

interface Point {
  x: number;
  y: number;
}

interface OCRDetection {
  bbox: Point[];
  text: string;
  score: number;
}

Loading the model

To load the model, use the load method. It accepts:

detectorSources - An object that specifies the location of the detectors binary files. Each detector is composed of two models tailored to process images of varying widths.

• detectorLarge - A string that specifies the location of the recognizer binary file which accepts input images with a width of 1280 pixels.
• detectorNarrow - A string that specifies the location of the detector binary file which accepts input images with a width of 320 pixels.

For more information, take a look at loading models section.

recognizerSources - An object that specifies the locations of the recognizers binary files. Each recognizer is composed of two models tailored to process images of varying widths.

• recognizerLarge - A string that specifies the location of the recognizer binary file which accepts input images with a width of 512 pixels.
• recognizerSmall - A string that specifies the location of the recognizer binary file which accepts input images with a width of 64 pixels.

For more information, take a look at loading models section.

language - A parameter that specifies the language of the text to be recognized by the OCR.

independentCharacters – A boolean parameter that indicates whether the text in the image consists of a random sequence of characters. If set to true, the algorithm will scan each character individually instead of reading them as continuous text.

This method returns a promise, which can resolve to an error or void.

Listening for download progress

To subscribe to the download progress event, you can use the onDownloadProgress method. It accepts a callback function that will be called whenever the download progress changes.

Running the model

To run the model, you can use the forward method. It accepts one argument, which is the image. The image can be a remote URL, a local file URI, or a base64-encoded image. The method returns a promise, which can resolve either to an error or an array of OCRDetection objects. Each object contains coordinates of the bounding box, the label of the detected object, and the confidence score.