useOCR

Optical character recognition(OCR) is a computer vision technique that detects and recognizes text within the image. It's commonly used to convert different types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data.

caution

It is recommended to use models provided by us, which are available at our Hugging Face repository. You can also use constants shipped with our library.

Reference

import { useOCR, OCR_ENGLISH } from 'react-native-executorch';

function App() {
  const model = useOCR({ model: OCR_ENGLISH });

  // ...
  for (const ocrDetection of await model.forward('https://url-to-image.jpg')) {
    console.log('Bounding box: ', ocrDetection.bbox);
    console.log('Bounding label: ', ocrDetection.text);
    console.log('Bounding score: ', ocrDetection.score);
  }
  // ...
}

Type definitions

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

Arguments

model - Object containing the detector source, recognizer sources, and language.

• detectorSource - A string that specifies the location of the detector binary.
• recognizerLarge - A string that specifies the location of the recognizer binary file which accepts input images with a width of 512 pixels.
• recognizerMedium - A string that specifies the location of the recognizer binary file which accepts input images with a width of 256 pixels.
• recognizerSmall - A string that specifies the location of the recognizer binary file which accepts input images with a width of 128 pixels.
• language - A parameter that specifies the language of the text to be recognized by the OCR.

preventLoad? - Boolean that can prevent automatic model loading (and downloading the data if you load it for the first time) after running the hook.

For more information on loading resources, take a look at loading models page.

Returns

The hook returns an object with the following properties:

Field	Type	Description
forward	(imageSource: string) => Promise<OCRDetection[]>	A function that accepts an image (url, b64) and returns an array of OCRDetection objects.
error	string | null	Contains the error message if the model loading failed.
isGenerating	boolean	Indicates whether the model is currently processing an inference.
isReady	boolean	Indicates whether the model has successfully loaded and is ready for inference.
downloadProgress	number	Represents the download progress as a value between 0 and 1.

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 function returns an array of OCRDetection objects. Each object contains coordinates of the bounding box, the text recognized within the box, and the confidence score. For more information, please refer to the reference or type definitions.

Detection object

The detection object is specified as follows:

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

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

The bbox property contains information about the bounding box of detected text regions. It is represented as four points, which are corners of detected bounding box. The text property contains the text recognized within detected text region. The score represents the confidence score of the recognized text.

Example

import { useOCR, OCR_ENGLISH } from 'react-native-executorch';

function App() {
  const model = useOCR({ model: OCR_ENGLISH });

  const runModel = async () => {
    const ocrDetections = await model.forward('https://url-to-image.jpg');

    for (const ocrDetection of ocrDetections) {
      console.log('Bounding box: ', ocrDetection.bbox);
      console.log('Bounding text: ', ocrDetection.text);
      console.log('Bounding score: ', ocrDetection.score);
    }
  };
}

Language-Specific Recognizers

Each supported language requires its own set of recognizer models.
The built-in constants such as RECOGNIZER_EN_CRNN_512, RECOGNIZER_PL_CRNN_256, etc., point to specific models trained for a particular language.

For example:

• To recognize English text, use:
  • RECOGNIZER_EN_CRNN_512
  • RECOGNIZER_EN_CRNN_256
  • RECOGNIZER_EN_CRNN_128
• To recognize Polish text, use:
  • RECOGNIZER_PL_CRNN_512
  • RECOGNIZER_PL_CRNN_256
  • RECOGNIZER_PL_CRNN_128

You need to make sure the recognizer models you pass in recognizerSources match the language you specify.

Supported languages

Language	Code Name
Abaza	abq
Adyghe	ady
Africans	af
Avar	ava
Azerbaijani	az
Belarusian	be
Bulgarian	bg
Bosnian	bs
Simplified Chinese	chSim
Chechen	che
Chech	cs
Welsh	cy
Danish	da
Dargwa	dar
German	de
English	en
Spanish	es
Estonian	et
French	fr
Irish	ga
Croatian	hr
Hungarian	hu
Indonesian	id
Ingush	inh
Icelandic	ic
Italian	it
Japanese	ja
Karbadian	kbd
Kannada	kn
Korean	ko
Kurdish	ku
Latin	la
Lak	lbe
Lezghian	lez
Lithuanian	lt
Latvian	lv
Maori	mi
Mongolian	mn
Malay	ms
Maltese	mt
Dutch	nl
Norwegian	no
Occitan	oc
Pali	pi
Polish	pl
Portuguese	pt
Romanian	ro
Russian	ru
Serbian (Cyrillic)	rsCyrillic
Serbian (Latin)	rsLatin
Slovak	sk
Slovenian	sl
Albanian	sq
Swedish	sv
Swahili	sw
Tabassaran	tab
Telugu	te
Thai	th
Tajik	tjk
Tagalog	tl
Turkish	tr
Ukrainian	uk
Uzbek	uz
Vietnamese	vi

Supported models

Model	Type
CRAFT_800*	Detector
CRNN_512*	Recognizer
CRNN_256*	Recognizer
CRNN_128*	Recognizer

* - The number following the underscore (_) indicates the input image width used during model export.

Benchmarks

Model size

Model	XNNPACK [MB]
Detector (CRAFT_800)	83.1
Recognizer (CRNN_512)	15 - 18*
Recognizer (CRNN_256)	16 - 18*
Recognizer (CRNN_128)	17 - 19*

* - The model weights vary depending on the language.

Memory usage

Model	Android (XNNPACK) [MB]	iOS (XNNPACK) [MB]
Detector (CRAFT_800) + Recognizer (CRNN_512) + Recognizer (CRNN_256) + Recognizer (CRNN_128)	1600	1700

Inference time

Image Used for Benchmarking:

Original Image	Image with detected Text Boxes

warning

Times presented in the tables are measured as consecutive runs of the model. Initial run times may be up to 2x longer due to model loading and initialization.

Time measurements:

Metric	iPhone 14 Pro Max [ms]	iPhone 16 Pro [ms]	iPhone SE 3	Samsung Galaxy S24 [ms]	OnePlus 12 [ms]
Total Inference Time	4330	2537	❌	6648	5993
Detector (CRAFT_800)	1945	1809	❌	2080	1961
Recognizer (CRNN_512)
├─ Average Time	273	76	❌	289	252
├─ Total Time (3 runs)	820	229	❌	867	756
Recognizer (CRNN_256)
├─ Average Time	137	39	❌	260	229
├─ Total Time (7 runs)	958	271	❌	1818	1601
Recognizer (CRNN_128)
├─ Average Time	68	18	❌	239	214
├─ Total Time (7 runs)	478	124	❌	1673	1498

❌ - Insufficient RAM.