PaddleOCR.js (browser deployment)

PaddleOCR provides PaddleOCR.js, a browser OCR SDK for running the PP-OCR pipeline in the browser. You can embed text detection and recognition in web apps and run inference on the client.

The npm package is @paddleocr/paddleocr-js. Source and demo live under paddleocr-js on GitHub.

Install

bash

npm install @paddleocr/paddleocr-js

Quick start

import { PaddleOCR } from "@paddleocr/paddleocr-js";

const ocr = await PaddleOCR.create({
  lang: "ch",
  ocrVersion: "PP-OCRv5",
  ortOptions: {
    backend: "auto"
  }
});

const [result] = await ocr.predict(fileOrBlob);
console.log(result.items);

predict resolves to an array of OcrResult (one per input image). A single Blob / File still produces a one-element array—use destructuring or results[0].

Construction options

Two styles: direct parameters to PaddleOCR.create({ ... }), or a pipelineConfig object.

1. Direct parameters

With direct parameters, you can specify models and set batch sizes, ORT options, and other runtime settings.

Model selection — lang + ocrVersion:

await PaddleOCR.create({
  lang: "ch",
  ocrVersion: "PP-OCRv5"
});

Model selection — built-in model names:

await PaddleOCR.create({
  textDetectionModelName: "PP-OCRv5_mobile_det",
  textRecognitionModelName: "PP-OCRv5_mobile_rec"
});

Custom models — provide a name and asset URL for each of detection and recognition:

await PaddleOCR.create({
  textDetectionModelName: "my_det_model",
  textDetectionModelAsset: {
    url: "https://example.com/models/my_det_model.tar"
  },
  textRecognitionModelName: "my_rec_model",
  textRecognitionModelAsset: {
    url: "https://example.com/models/my_rec_model.tar"
  }
});

Batch sizes, ORT options, and other runtime settings:

await PaddleOCR.create({
  lang: "ch",
  ocrVersion: "PP-OCRv5",
  textDetectionBatchSize: 2,
  textRecognitionBatchSize: 8,
  ortOptions: {
    backend: "wasm",
    wasmPaths: "/assets/"
  }
});

Custom model archive format and validation

The SDK downloads textDetectionModelAsset.url / textRecognitionModelAsset.url over HTTP(S) and parses the body as a plain ustar tar (uncompressed) archive. Ensure that:

Requirement	Details
Archive format	The response body must be an uncompressed `.tar`. The implementation does not gunzip `.tar.gz`; if you pass a gzip-compressed tarball, parsing will typically fail and an error will be thrown.
Required files	The tar must contain `inference.onnx` and `inference.yml` (they may live in a subdirectory; entries are matched by basename).
`model_name`	`inference.yml` must define a `model_name` that matches the `textDetectionModelName` / `textRecognitionModelName` you pass to `create`. This is checked after load during initialization.

If you need to convert Paddle models into the ONNX model files used here, see Obtaining ONNX models. The standard model files produced by that workflow can then be packaged as a .tar following the rules above for use with PaddleOCR.js.

If the archive or model files do not meet these rules, initialization typically fails with an Error that describes the problem, for example: non-2xx download, missing inference.onnx / inference.yml in the tar, empty resources, missing or mismatched model_name, incomplete model config, or ONNX load failure. There is no silent fallback.

All selected OCR models must satisfy the model_name rules above.

2. Pipeline config

import { PaddleOCR } from "@paddleocr/paddleocr-js";

const pipelineConfig = `
pipeline_name: OCR
SubModules:
  TextDetection:
    model_name: PP-OCRv5_mobile_det
    batch_size: 2
  TextRecognition:
    model_name: PP-OCRv5_mobile_rec
    batch_size: 6
`;

const ocr = await PaddleOCR.create({ pipelineConfig });

pipelineConfig can be YAML text or a parsed object. In the browser, submodule model_dir must be null or an asset object (e.g. { url: "..." }), not a local filesystem path string. If you want to start from a pipeline configuration exported by PaddleOCR / PaddleX, see the "Exporting Pipeline Configuration Files" section in PaddleOCR and PaddleX; the exported YAML can be used as the basis for pipelineConfig, and any model_dir entries should then be adapted to browser-side asset objects.

If both direct parameters and pipelineConfig are provided, direct parameters take precedence.

Prediction

Params

ocr.predict(image | images[], params?) accepts both camelCase and PaddleOCR-style snake_case:

textDetLimitSideLen or text_det_limit_side_len
textDetLimitType or text_det_limit_type
textDetMaxSideLimit or text_det_max_side_limit
textDetThresh or text_det_thresh
textDetBoxThresh or text_det_box_thresh
textDetUnclipRatio or text_det_unclip_ratio
textRecScoreThresh or text_rec_score_thresh

Supported image inputs include Blob, ImageBitmap, ImageData, HTMLCanvasElement, HTMLImageElement, and cv.Mat. Pass an array to run on multiple images in one call.

In worker mode, cv.Mat is not transferable and is not supported as input.

Return value

Resolves to Promise<OcrResult[]>. Each OcrResult contains:

image: { width, height } for that source
items: recognized lines (poly, text, score)
metrics: detMs, recMs, totalMs, detectedBoxes, recognizedCount — box and line counts are per image; detMs, recMs, and totalMs cover the entire predict() call (identical on every element when you pass multiple images)
runtime: requested backend and provider metadata

Worker mode

You can run the pipeline inside a dedicated Worker while keeping the same high-level API:

import { PaddleOCR } from "@paddleocr/paddleocr-js";

const ocr = await PaddleOCR.create({
  lang: "ch",
  ocrVersion: "PP-OCRv5",
  worker: true,
  ortOptions: {
    backend: "wasm",
    wasmPaths: "https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/",
    numThreads: 2,
    simd: true
  }
});

Summary:

Worker mode uses the package worker entry, not ONNX Runtime Web env.wasm.proxy
When worker: true, the package forces ORT wasm proxy off to avoid nested workers
Browser inputs are normalized on the main thread, then transferred to the worker
cv.Mat is only supported on the main-thread pipeline path

Visualization

The optional @paddleocr/paddleocr-js/viz subpath renders OCR results to images.

import { OcrVisualizer } from "@paddleocr/paddleocr-js/viz";

const viz = new OcrVisualizer({
  font: { family: "Noto Sans SC", source: "/fonts/NotoSansSC-Regular.ttf" }
});

const blob = await viz.toBlob(imageBitmap, result);
const url = URL.createObjectURL(blob);
const a = document.createElement("a");
a.href = url;
a.download = "ocr_result.png";
a.click();
URL.revokeObjectURL(url);

viz.dispose();

renderOcrToBlob and deterministicColor are also exported. Visualization takes a single OcrResult (for one image, use the first element of the predict result array).

API summary

PaddleOCR.create(options)
ocr.initialize() / ocr.getInitializationSummary()
ocr.predict(image | images[], params?) → Promise<OcrResult[]>
ocr.dispose()
parseOcrPipelineConfigText(text) / normalizeOcrPipelineConfig(config)
OcrVisualizer, renderOcrToBlob, deterministicColor (from @paddleocr/paddleocr-js/viz)

Host application responsibilities

The SDK manages OpenCV.js and ONNX Runtime internally. You still handle:

COOP/COEP (and related headers) when enabling threaded WASM or WebGPU
ORT environment options (e.g. wasmPaths, threads, SIMD)
A bundler/runtime that can emit and load module workers when worker: true