ppu-paddle-ocr

v5.8.3

Published

12 days ago

Lightweight, probably the fastest PaddleOCR SDK in TypeScript. Runs anywhere JavaScript runs: Node.js, Bun, Deno, web browsers, and browser extensions. Docker & CLI supported. The official SDK is browser-only. Accurate text detection and recognition for d

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ppu-paddle-ocr

Need it as HTTP-service? dockerized? we've got you covered! Quickly spins up ppu-paddle-ocr REST API here: ppu-paddle-ocr-serve. Need a CLI instead? sure here: ppu-paddle-ocr CLI support.

ppu-paddle-ocr demo

import { PaddleOcrService } from "ppu-paddle-ocr";

const service = new PaddleOcrService();
await service.initialize();

const result = await service.recognize("./receipt.jpg");
console.log(result.text);

await service.destroy();

Why ppu-paddle-ocr?

Lightweight — minimal dependencies, optimized for performance.
Pre-packed models — PP-OCRv5 mobile models (English) are fetched and cached automatically on first run. Supports 40+ languages via ppu-paddle-ocr-models.
Runs everywhere — Node.js, Bun, Deno, web browsers, and browser extensions. The official SDK is browser-only.
Customizable — custom models, dictionaries, and per-call overrides.
TypeScript — full type definitions.

Runtime Support

The same package, the same API, every JavaScript runtime:

| Runtime | How to install | Try it | | --------------------- | ----------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | | Node.js | npm install ppu-paddle-ocr onnxruntime-node | npm package | | Bun | bun add ppu-paddle-ocr onnxruntime-node | npm package | | Deno | deno add jsr:@snowfluke/ppu-paddle-ocr | JSR package | | Web browser | npm install ppu-paddle-ocr onnxruntime-web (import /web subpath) | Live demo | | Browser extension | Same as web; bundle ppu-paddle-ocr/web with your extension's bundler. | Example extension repo |

Installation

npm install ppu-paddle-ocr onnxruntime-node onnxruntime-web

Omit onnxruntime-node or onnxruntime-web depending on your target environment (Node/Bun vs browser).

CLI (global install)

To use the command line without bunx/npx, install globally — this puts a ppu-paddle-ocr command on your PATH:

npm install -g ppu-paddle-ocr      # or: bun add -g ppu-paddle-ocr
ppu-paddle-ocr recognize receipt.jpg

onnxruntime-node is pulled automatically (it's an optional dependency), so nothing else is needed. Notes:

bun: ensure ~/.bun/bin is on your PATH (npm's global bin usually already is).
Updates are manual — re-run the install with @latest to upgrade. (bunx/npx always fetch the latest but can serve a stale cache; a global install pins the version and you own upgrades.)
It's still the Node/Bun build — a global install gives you a global command, not a standalone binary, so Node or Bun must be present.

Core Usage

Basic Recognition

import { PaddleOcrService } from "ppu-paddle-ocr";

const service = new PaddleOcrService({
  debugging: {
    debug: false,
    verbose: true,
  },
});

await service.initialize();

const result = await service.recognize("./assets/receipt.jpg");
console.log(result.text);

await service.destroy();

Custom Models

Pass file paths, URLs, or ArrayBuffers for the detection model, recognition model, and dictionary:

const service = new PaddleOcrService({
  model: {
    detection: "./models/custom-det.onnx",
    recognition: "https://example.com/models/custom-rec.onnx",
    charactersDictionary: customDictArrayBuffer,
  },
});

await service.initialize();

Changing Models at Runtime

const service = new PaddleOcrService();
await service.initialize();

await service.changeDetectionModel("./models/new-det.onnx");
await service.changeRecognitionModel("./models/new-rec.onnx");
await service.changeTextDictionary("./models/new-dict.txt");

Per-Call Options

Each recognize() call accepts RecognizeOptions for fine-grained control:

// Custom dictionary for one-off recognition
const result = await service.recognize("./assets/receipt.jpg", {
  dictionary: "./models/new-dict.txt",
});

// Disable caching for fresh processing
const fresh = await service.recognize("./assets/receipt.jpg", {
  noCache: true,
});

// Combine options
const result = await service.recognize("./assets/receipt.jpg", {
  noCache: true,
  flatten: true,
  strategy: "per-box",
});

Command Line

The package ships a bin, so you can OCR without writing any code — bunx/npx resolve it directly (no global install):

# one image → recognized text on stdout
bunx ppu-paddle-ocr recognize receipt.jpg

# a URL, as structured JSON
npx ppu-paddle-ocr recognize https://example.com/invoice.png --json --pretty

# many images (glob), fastest strategy, written to a file
bunx ppu-paddle-ocr batch "scans/*.png" --strategy cross-line --json -o results.json

# print each result as it finishes
bunx ppu-paddle-ocr stream "scans/*.png"

# pre-warm / clear the model cache, inspect the active config
bunx ppu-paddle-ocr download-models
bunx ppu-paddle-ocr clear-cache
bunx ppu-paddle-ocr models --json

Every PaddleOptions / RecognizeOptions field maps to a flag: --strategy, --engine, --flatten, --no-cache, --image-height, --model-detection/-recognition/-dict, detection tuning (--max-side-length, --padding-vertical, --padding-horizontal, --min-area, --mean, --std), --execution-providers, and for batch/stream --concurrency. Output is controlled by --json, --pretty, -o/--output, -q/--quiet, and --verbose.

Recognized text goes to stdout; progress and logs go to stderr, so output pipes cleanly. Exit codes: 0 success, 1 runtime error, 2 usage error. Run bunx ppu-paddle-ocr help for the full reference. The CLI uses the default v5 models unless you override the --model-* flags.

Batch Recognition

batchRecognize() runs recognize() over many images with bounded concurrency, so memory stays in check: at most concurrency images are decoded and in flight at once. Results are returned index-aligned to the inputs regardless of completion order.

const results = await service.batchRecognize([buf1, buf2, buf3]);
results.forEach((r, i) => console.log(i, r.text));

Concurrency defaults to "auto" — 1 when an accelerator provider (CUDA, WebGPU) is configured (a shared session serializes device work anyway, and parallel runs would stack VRAM), and a small CPU default otherwise to overlap JS preprocessing with native inference. Override it explicitly when you know your hardware:

await service.batchRecognize(images, { concurrency: 8, flatten: true });

Use settle: true to keep going when an image fails — each slot becomes { status, value | reason } instead of the call rejecting:

const results = await service.batchRecognize(images, { settle: true });
for (const r of results) {
  if (r.status === "fulfilled") console.log(r.value.text);
  else console.error("failed:", r.reason);
}

Track progress and cancel with the usual primitives:

const ac = new AbortController();
await service.batchRecognize(images, {
  signal: ac.signal,
  onProgress: (done, total) => console.log(`${done}/${total}`),
});

To consume results as they finish (and avoid buffering the whole batch), stream them — each item carries its input index for reordering:

for await (const item of service.batchRecognizeStream(images)) {
  if (item.status === "fulfilled") console.log(item.index, item.value.text);
}

batchRecognize / batchRecognizeStream also accept any Iterable or AsyncIterable of inputs, so a directory walk or queue never has to be materialized in memory at once. All RecognizeOptions (flatten, strategy, dictionary, noCache) are accepted and applied to every image. See BatchRecognizeOptions for the full surface.

Recognition Strategies

Recognition strategies control how detected text regions are cropped from the canvas and fed into the recognition model. Fewer inference calls means faster throughput.

Default: per-line (best accuracy/speed trade-off).

Strategies are set in RecognitionOptions:

const service = new PaddleOcrService({
  recognition: { strategy: "cross-line" },
});
await service.initialize();

recognition strategies

Image Preprocessing

PaddleOCR works best with grayscale or thresholded images. Use ppu-ocv for preprocessing before recognition:

import { ImageProcessor, CanvasProcessor } from "ppu-ocv";
const processor = new ImageProcessor(bodyCanvas);

// For non-OpenCV environments (e.g. browser extensions)
// const processor = new CanvasProcessor(bodyCanvas)

processor.grayscale().blur();
const canvas = processor.toCanvas();
processor.destroy();

Processing Engine

Two image processing backends are available for detection preprocessing and recognition resizing:

| Engine | Default | OpenCV Required | Notes | | :---------------- | :-----: | :-------------: | :-------------------------------------------------- | | "opencv" | Yes | Yes | Uses OpenCV.js from ppu-ocv. More accurate boxes. | | "canvas-native" | No | No | Pure canvas from ppu-ocv/canvas. Lighter weight. |

The browser build (ppu-paddle-ocr/web) always uses canvas-native — OpenCV.js is not bundled in the web entry point.

// OpenCV (default, recommended)
const service = new PaddleOcrService();

// Canvas-native (no OpenCV dependency)
const service = new PaddleOcrService({
  processing: { engine: "canvas-native" },
});

Web / Browser Support

Import from ppu-paddle-ocr/web for browser-native capabilities (HTMLCanvasElement, OffscreenCanvas, fetch buffering).

Using a Bundler (Vite, Webpack, etc.)

import { PaddleOcrService } from "ppu-paddle-ocr/web";

const service = new PaddleOcrService();
await service.initialize();

const file = document.getElementById("upload").files[0];

const img = new Image();
img.src = URL.createObjectURL(file);
await new Promise((r) => (img.onload = r));

const canvas = document.createElement("canvas");
canvas.width = img.width;
canvas.height = img.height;
canvas.getContext("2d").drawImage(img, 0, 0);

const result = await service.recognize(canvas);
console.log(result.text);

CDN (No Bundler)

See the live demo for a complete ESM/CDN setup.

WebGPU Acceleration

On WebGPU-capable browsers (Chrome/Edge on Windows/Linux/macOS, Firefox Nightly), ONNX inference automatically runs on the GPU — typically 2–5× faster with no code changes. The library silently falls back to WASM if WebGPU is unavailable or fails.

Detection runs once during initialize() and is fully transparent.

import { isWebGpuAvailable, getDefaultWebExecutionProviders } from "ppu-paddle-ocr/web";

if (await isWebGpuAvailable()) {
  console.log("WebGPU supported");
}

Override Provider Preference

// Force WASM-only
const service = new PaddleOcrService({
  session: {
    executionProviders: ["wasm"],
    graphOptimizationLevel: "all",
  },
});

The WASM binaries are still required even when WebGPU is the primary provider (used for graph optimization and fallback ops). Set ort.env.wasm.wasmPaths before initialize() if you self-host them.

Multithreaded WASM (Cross-Origin Isolation)

When the WASM backend is used (no WebGPU, or executionProviders: ["wasm"]), ONNX Runtime only runs multithreaded if the page is cross-origin isolated — otherwise numThreads is pinned to 1. Cross-origin isolation requires the Cross-Origin-Opener-Policy: same-origin and Cross-Origin-Embedder-Policy: require-corp response headers.

If you can set those headers server-side, do that — it's the correct fix and needs nothing from this package. WebGPU does not need isolation at all, so this only matters on the WASM fallback path.

For static hosts that can't set headers (e.g. GitHub Pages), the package ships an opt-in coi-serviceworker that injects the headers client-side. Copy it to your served root and load it from your page before anything else:

<script src="/coi-serviceworker.js"></script>

Resolve the shipped copy from the package, e.g. in a build step:

// path on disk: node_modules/ppu-paddle-ocr/coi-serviceworker.js
const swPath = import.meta.resolve("ppu-paddle-ocr/coi-serviceworker.js");

The service worker reloads the page once on first visit to apply the headers and rewrites all fetch responses. Don't use it if you already control your headers or run another service worker that conflicts.

Models and Language Support

Default Models

The default PP-OCRv5 mobile models are optimized for English and served in ONNX Runtime's .ort FlatBuffers format (3–5× faster session creation than .onnx):

Portable .onnx variants are available at ppu-paddle-ocr-models — point model.detection / model.recognition at the .onnx URLs.

Cache Location (Node / Bun)

Models are cached under ~/.cache/ppu-paddle-ocr:

// Warm the cache (e.g. in CI or Docker builds)
PaddleOcrService.downloadModels();

// Clear the cache
service.clearModelCache();

In the browser, model files are fetched via fetch() on every page load and rely on the browser's HTTP cache. For persistent offline caching, use a Service Worker or store the ArrayBuffer in IndexedDB.

Multilingual Support

PP-OCRv5 supports 40+ languages across different script systems. Pre-converted ONNX models are available at ppu-paddle-ocr-models:

Latin: English, French, German, Italian, Spanish, Portuguese, and 40+ others
Cyrillic: Russian, Ukrainian, Bulgarian, Kazakh, Serbian, and 30+ related
Arabic: Arabic, Persian, Urdu, Kurdish
Indic: Hindi (Devanagari), Tamil, Telugu
East Asian: Korean, Japanese
Southeast Asian: Thai

Switching Languages

const MODEL_BASE =
  "https://media.githubusercontent.com/media/PT-Perkasa-Pilar-Utama/ppu-paddle-ocr-models/refs/heads/main";
const DICT_BASE =
  "https://raw.githubusercontent.com/PT-Perkasa-Pilar-Utama/ppu-paddle-ocr-models/refs/heads/main";

// Thai
const service = new PaddleOcrService({
  model: {
    detection: `${MODEL_BASE}/detection/PP-OCRv5_mobile_det_infer.onnx`,
    recognition: `${MODEL_BASE}/recognition/multi/thai/v5/th_PP-OCRv5_mobile_rec_infer.onnx`,
    charactersDictionary: `${DICT_BASE}/recognition/multi/thai/v5/ppocrv5_th_dict.txt`,
  },
});

Server Models (Higher Accuracy)

PP-OCRv5 is available in mobile and server variants:

const service = new PaddleOcrService({
  model: {
    detection: `${MODEL_BASE}/detection/PP-OCRv5_server_det_infer.onnx`,
    recognition: `${MODEL_BASE}/recognition/multi/en/v5/en_PP-OCRv5_server_rec_infer.onnx`,
    charactersDictionary: `${DICT_BASE}/recognition/multi/en/v5/ppocrv5_en_dict.txt`,
  },
});

INT8 Quantization

The recognition model's transformer MatMul operations can be dynamically quantized to INT8 with no accuracy loss (measured 99.22% → 99.22%) and a 20–50% speedup on x86-64 CPUs with VNNI and WebAssembly.

On Apple Silicon (M-series), INT8 is not faster — the FP32 NEON/Accelerate kernels outperform the INT8 MLAS path. Stick with FP32 on macOS ARM64.

Run the quantization helper:

pip install onnxruntime onnx sympy
python examples/quantize-onnx.py /path/to/en_PP-OCRv5_mobile_rec_infer.onnx
# -> produces en_PP-OCRv5_mobile_rec_infer_int8.onnx

Use the quantized model via model.recognition:

const service = new PaddleOcrService({
  model: {
    recognition: "https://example.com/en_PP-OCRv5_mobile_rec_infer_int8.onnx",
  },
});

INT8 .ort variants are also available in the ppu-paddle-ocr-models repo.

Model Output Limitations

Tables: Text within table cells is detected, but table structure is not preserved.
Math formulas: Not optimized for mathematical notation.
Document layout: For layout detection, see PP-DocLayoutV2/V3 models in ppu-paddle-ocr-models.

Converting Custom PaddlePaddle Models

See the ONNX conversion guide.

Configuration Reference

`PaddleOptions`

import type { PaddleOptions } from "ppu-paddle-ocr";

export type PaddleOptions = {
  model?: ModelPathOptions;
  detection?: DetectionOptions;
  recognition?: RecognitionOptions;
  debugging?: DebuggingOptions;
  session?: SessionOptions;
  processing?: ProcessingOptions;
};

`RecognizeOptions`

Per-call options for recognize().

`BatchRecognizeOptions`

Extends RecognizeOptions (applied to every image) for batchRecognize() / batchRecognizeStream().

| Property | Type | Default | Description | | :------------ | :----------------------: | :------: | :--------------------------------------------------------------------------------------- | | concurrency | number \| "auto" | "auto" | Max images in flight. "auto" = 1 on an accelerator provider, small default on CPU. | | settle | boolean | false | When true, a failed image yields { status: "rejected", reason } instead of throwing. | | signal | AbortSignal | null | Cancels the batch; pending images are not scheduled and the call rejects. | | onProgress | (done, total?) => void | null | Called after each image settles, with the running count and total (if known). |

`ModelPathOptions`

Leave a trailing newline in your dictionary file.

`DetectionOptions`

Controls preprocessing and filtering during text detection.

`RecognitionOptions`

Controls recognition preprocessing and strategy.

`DebuggingOptions`

`SessionOptions`

Any valid ONNX Runtime InferenceSession.SessionOptions property is accepted. ppu-paddle-ocr sets these defaults:

const service = new PaddleOcrService({
  session: {
    executionProviders: ["cpu"],
    graphOptimizationLevel: "all",
    enableCpuMemArena: true,
    enableMemPattern: true,
    executionMode: "sequential",
  },
});

`ProcessingOptions`

Benchmark

Benches use a small zero-dependency harness (bench/harness.ts): in-process timing, round-robin scheduling across rounds so thermal/GC drift hits every task equally, reporting the median plus min/max/stddev. Run bun task bench. Representative results on Apple M1 / Bun 1.3.14 (20 rounds, opencv + canvas-native):

task                                   median      ±stddev        min        max
--------------------------------------------------------------------------------
[per-box][opencv][noCache]             233.0 ms      14.6 ms   211.2 ms   254.5 ms
[per-line][opencv][noCache]            224.7 ms      17.6 ms   194.3 ms   256.0 ms
[cross-line][opencv][noCache]          213.9 ms      18.7 ms   194.7 ms   266.3 ms
[per-box][canvas-native][noCache]      242.3 ms      22.0 ms   213.3 ms   301.1 ms
[per-line][canvas-native][noCache]     224.3 ms      13.9 ms   201.9 ms   245.4 ms
[cross-line][canvas-native][noCache]   223.3 ms      14.4 ms   198.3 ms   248.6 ms

=== Accuracy on receipt.jpg (ground truth: 383 chars) ===
  [opencv]       per-box=97.91%  per-line=99.22%  cross-line=96.34%
  [canvas-native] per-box=97.65% per-line=98.43%  cross-line=97.65%

Absolute timings are thermal-sensitive on fanless hardware (Apple Silicon): sustained benching warms the chip and drags the median up, while the min column tracks the unthrottled per-call cost (~195 ms here). Treat these as relative, same-run comparisons, not cross-machine absolutes.

Batch vs. concurrent `recognize()`

bench/batch.bench.ts compares the ways to OCR many images, tracking peak RSS alongside time. Default models (v5), median over 7 rounds of 16 images each, Apple M1 / Bun 1.3.14, opencv, noCache:

task                          median      ±stddev        min        max   peak RSS
----------------------------------------------------------------------------------
sequential for-loop          3802.5 ms     300.6 ms  3169.4 ms  3979.7 ms    1059 MB
Promise.all(map(recognize))  3543.5 ms     254.0 ms  3030.0 ms  3768.0 ms    1428 MB
batchRecognize (auto)        3676.1 ms     200.9 ms  3217.1 ms  3761.3 ms    1096 MB
batchRecognize (c=4)         3653.8 ms     239.1 ms  3170.1 ms  3804.1 ms    1027 MB
batchRecognize (c=8)         3605.7 ms     187.6 ms  3202.1 ms  3786.6 ms    1096 MB

On CPU, throughput is bound by ONNX Runtime's native thread pool (which already saturates all cores per inference), so every parallel approach lands within ~4% on time — JS-level concurrency cannot add cores that are already busy. The real difference is memory: unbounded Promise.all peaks at ~1430 MB and grows with batch size, while batchRecognize stays bounded at ~1030–1100 MB regardless of N. So batchRecognize matches the fastest approach at lower, bounded peak memory — and the throughput win from concurrency shows up on GPU (overlapping host↔device) or I/O-bound inputs. Tune BATCH_N / ROUNDS via env.

Contributing

See CONTRIBUTING.md for setup instructions, code-quality requirements, and the pull request process.

License

MIT — see LICENSE.

Support

Open an issue or join our Slack community.

Scripts

Recommended development environment is Linux-based. Library template: https://github.com/aquapi/lib-template

To run a specific benchmark file:

bun task bench index     # Run bench/index.bench.ts
bun task bench --node    # Run all benchmarks with Node.js

Published

Vulnerabilities

Links

Maintainers

Keywords

Readme

ppu-paddle-ocr

Table of Contents

Why ppu-paddle-ocr?

Runtime Support

Installation

CLI (global install)

Core Usage

Basic Recognition

Custom Models

Changing Models at Runtime

Per-Call Options

Command Line

Batch Recognition

Recognition Strategies

Image Preprocessing

Processing Engine

Web / Browser Support

Using a Bundler (Vite, Webpack, etc.)

CDN (No Bundler)

WebGPU Acceleration

Override Provider Preference

Multithreaded WASM (Cross-Origin Isolation)

Models and Language Support

Default Models

Cache Location (Node / Bun)

Multilingual Support

Switching Languages

Server Models (Higher Accuracy)

INT8 Quantization

Model Output Limitations

Converting Custom PaddlePaddle Models

Configuration Reference

PaddleOptions

RecognizeOptions

BatchRecognizeOptions

ModelPathOptions

DetectionOptions

RecognitionOptions

DebuggingOptions

SessionOptions

ProcessingOptions

Benchmark

Batch vs. concurrent recognize()

Contributing

License

Support

Scripts

`PaddleOptions`

`RecognizeOptions`

`BatchRecognizeOptions`

`ModelPathOptions`

`DetectionOptions`

`RecognitionOptions`

`DebuggingOptions`

`SessionOptions`

`ProcessingOptions`

Batch vs. concurrent `recognize()`