PaddleOcr

中文文档 (Chinese Documentation)

A lightweight, type-safe, dependency-free JavaScript/TypeScript library for PaddleOCR, supporting both Node.js and browser environments.

Features

• Cross-platform: Works in Node.js, Bun, and browser environments.
• Type-safe: Written in TypeScript with full type definitions.
• No dependencies: Minimal footprint, no heavy image processing libraries included.
• Flexible model loading: Accepts model files as ArrayBuffer, allowing custom loading strategies (e.g., fetch, fs.readFileSync).
• ONNX Runtime support: Compatible with both onnxruntime-web and onnxruntime-node.
• Customizable dictionary: Pass your own character dictionary for recognition.
• Modern API: Simple, promise-based API for easy integration.

Installation

npm install paddleocr
# or
yarn add paddleocr
# or
pnpm add paddleocr

Usage

1. Prepare ONNX Runtime and Model Files

• In browser:

  import * as ort from "onnxruntime-web";

• In Node.js or Bun:

  import * as ort from "onnxruntime-node";

2. Load Model Files and Dictionary

You can use fetch, fs.readFileSync, or any other method to load your ONNX model files and dictionary as ArrayBuffer and string array, respectively.

3. Initialize the Service

import { PaddleOcrService } from "paddleocr";

const paddleOcrService = await PaddleOcrService.createInstance({
    ort,
    detection: {
        modelBuffer: detectOnnx,
    },
    recognition: {
        modelBuffer: recOnnx,
        charactersDictionary: dict,
    },
});

4. Prepare Image Data

The recognize method expects an object with width, height, and data (Uint8Array of RGB(A) values). Use your preferred image decoding library (e.g., fast-png, image-js).

import { decode } from "fast-png";
const imageFile = await readFile("tests/image.png");
const buffer = imageFile.buffer.slice(imageFile.byteOffset, imageFile.byteOffset + imageFile.byteLength);
const image = decode(buffer);
const input = {
    data: image.data,
    width: image.width,
    height: image.height,
};

5. Run OCR

const result = await paddleOcrService.recognize(input);
console.log(result);

Model Files

You can find sample models in the assets/ directory:

• PP-OCRv5_mobile_det_infer.onnx
• PP-OCRv5_mobile_rec_infer.onnx
• ppocrv5_dict.txt

Examples

See the examples/ directory for usage samples. About browser usage with Vite, check out paddleocr-vite-example

Contributing

Contributions are welcome! Feel free to submit a PR or open an issue.

License

MIT