faster-paddle 0.0.13

Fast CPU OCR — PaddleOCR PP-OCRv6 tiny (lightweight), reimplemented in Rust + ONNX Runtime. ~7x faster than PaddlePaddle, self-contained wheels with models bundled.

faster-paddle

Fast, CPU-only OCR in Rust with Python bindings — a self-contained reimplementation of PaddleOCR's PP-OCRv6 detection + recognition pipeline powered by ONNX Runtime.

• ⚡ ~9× faster than paddleocr on CPU for the same models and output (parallel detection pre/post-processing + a concurrent recognition session pool).
• 📦 Self-contained — the tiny + small ONNX models are bundled inside the wheel. No paddlepaddle, no model downloads for tiny/small.
• 🎚️ Three model sizes: tiny (default, fastest), small, and medium (higher accuracy; downloaded once on first use and cached).
• 🦀 Pure-Rust pre/post-processing (detection DB decode, minAreaRect, perspective crop, CTC decode, reading-order text reconstruction). No OpenCV.
• 🖥️ Prebuilt wheels for Linux, Windows, macOS (x86-64 + arm64).

paddleocr (PaddlePaddle, CPU)        22.7 s / image
faster-paddle (Rust + ONNXRuntime)    2.5 s / image     →  ~9× faster

(test image 3157×4464, AMD Ryzen 7 5800X3D; both after warm-up, same weights.)

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Install

pip install faster-paddle

Usage

import faster_paddle

# One-shot, using a shared default engine (lazily initialized):
with open("document.jpg", "rb") as f:
    result = faster_paddle.ocr(f.read())

print(result["text"])              # reading-order reconstructed text
for idx, b in result["bounds"].items():
    print(idx, b["text"], b["confidence"], b["topLeftCoord"], b["bottomRightCoord"])

Reuse an explicit engine (recommended for servers — load the models once):

from faster_paddle import OcrEngine

# model_size: "tiny" (default), "small", or "medium"
engine = OcrEngine(model_size="tiny", threads=None, det_max_side=1600)

result = engine.ocr(image_bytes)                 # raw jpeg/png/webp/bmp/tiff/gif bytes
result = engine.ocr_base64(b64_string)           # base64-encoded image

Optional preprocessing

ocr / ocr_base64 take four optional flags (all default False), applied — when enabled — in the optimal order, all in fast parallel Rust:

result = engine.ocr(
    image_bytes,
    resize=True,     # 1. downscale to ≤ 2100×3000 (aspect preserved) if larger
    denoise=True,    # 2. fast Non-Local-Means denoise (grayscale)
    deskew=True,     # 3. detect skew (Canny + Hough) and rotate to straighten
    binarize=True,   # 4. Sauvola adaptive thresholding (clean black/white)
)

Order rationale: resize first (everything downstream is then faster), denoise before angle detection and thresholding, deskew on the cleaned image, binarize last to produce the final B/W. Enabling resize typically makes OCR faster overall (less detector work). Any of denoise/deskew/binarize converts the image to grayscale.

Returned bounds are always in the original image's coordinate space — even when resize or deskew changes the working image, the boxes are mapped back so they line up with your input.

Preprocess only (no OCR)

prepare runs the same preprocessing in one pass and returns the prepared image as PNG bytes (grayscale once any of denoise/deskew/binarize is on, else color). If every option is False the original bytes are returned unchanged.

prepared = engine.prepare(image_bytes, resize=True, denoise=True, deskew=True, binarize=False)
# or module-level:  faster_paddle.prepare(image_bytes, resize=True, ...)

with open("prepared.png", "wb") as f:
    f.write(prepared)
# you can also feed it straight back in:
result = engine.ocr(prepared)

Model sizes

size	bundled	det+rec	notes
tiny	✅ yes	~6 MB	default, fastest, lightweight
small	✅ yes	~31 MB	better accuracy
medium	⬇️ on demand	~138 MB	best accuracy; downloaded once from the GitHub release and cached under your user cache dir

tiny and small are embedded in the wheel (offline). medium exceeds PyPI's file-size limit, so the first OcrEngine(model_size="medium") downloads it once (needs network that time only) and caches it for subsequent runs.

Result shape

{
  "text": "full reconstructed text...",
  "structured_text": "layout-preserving text (see below)",
  "bounds": {
     0: {
        "topLeftCoord":     (x1, y1),
        "bottomRightCoord": (x2, y2),
        "text":             "line text",
        "confidence":       0.97,
     },
     1: { ... },
  }
}

text and bounds match the JSON contract of the original paddle-ocr-api service, so it is a drop-in replacement.

structured_text

A spatial reconstruction that reads left-to-right, top-to-bottom while preserving the visual layout: vertical whitespace gaps split the page into columns/panes (each read fully before the next), and within each one the rows are laid out as a monospace grid, so indentation (tree nesting) and aligned sub-columns (key/value tables) are kept. Single-glyph UI icon noise is dropped.

Use structured_text for screenshots, forms, table/tree UIs, and code — anything where spatial structure carries meaning. Use text for dense multi-column prose: there the absolute pixel spacing of structured_text produces very wide lines, so the column-merging text reconstruction reads better. Both are always returned, so you can pick per use case.

Example structured_text for a two-pane database UI:

PNS
 Collections (11)
   System
   CAGED
   IPCMAPS_MUNICIPIO
 Functions
 Users

Key                                                Value
        OUTRAS_DESPESAS_POTENCIAL_DE_CONSUMO_EM... 7332964
        TOTAL_DO_CONSUMO_URBANO_E_RURAL            613855113
        CD_MUNI_IBGE                               1100015

API

faster_paddle.ocr(image, resize=False, denoise=False, deskew=False, binarize=False) -> dict	OCR encoded image bytes (shared default engine).
faster_paddle.ocr_base64(image_base64, resize=False, denoise=False, deskew=False, binarize=False) -> dict	OCR a base64 image string.
OcrEngine(model_size="tiny", threads=None, rec_batch=None, det_max_side=None)	Construct a reusable engine.
OcrEngine.ocr(image, resize=False, denoise=False, deskew=False, binarize=False) -> dict	OCR encoded image bytes.
OcrEngine.ocr_base64(image_base64, resize=False, denoise=False, deskew=False, binarize=False) -> dict	OCR a base64 image string.
faster_paddle.prepare(image, resize=False, denoise=False, deskew=False, binarize=False) -> bytes	Preprocess only; returns PNG bytes (no OCR).
OcrEngine.prepare(image, resize=False, denoise=False, deskew=False, binarize=False) -> bytes	Preprocess only; returns PNG bytes (no OCR).

• resize/denoise/deskew/binarize: optional preprocessing (see above).
• model_size: "tiny" (default), "small", or "medium".
• threads: ONNX Runtime intra-op threads. Defaults to the number of physical CPU cores (SMT/logical threads tend to slow compute-bound inference down).
• rec_batch: recognition batch cap (default 4; batching is otherwise adaptive).
• det_max_side: cap on the detector's longer side (default 1600). Large images are downscaled to this for detection only — recognition still crops from the full-resolution image, so text stays sharp. This makes detection ~2× faster on typical documents with negligible quality loss (the tiny detector locates text just as well below its useful resolution). Never upscales. Raise toward 4000 (PaddleOCR's default) for microscopic text, or lower (e.g. 1280) for more speed.

Calls are thread-safe (serialized internally) and release the GIL during inference.

Parallelism (automatic, hardware-scaled)

All parallelism is derived from the detected hardware — nothing is hardcoded:

• ONNX Runtime threads default to the number of physical cores.
• Recognition runs across a pool of ONNX Runtime sessions sized to target ~4 threads per session (cores/4, capped for memory), so it scales with the core count. Crops are grouped by a pixel budget so narrow crops batch together while wide line-crops run nearly alone (no wasted padding compute).
• The pre/post-processing (resize, denoise, deskew, binarize, DB decode, crop extraction) runs on rayon, scaled to the logical cores.

Override via OCR_THREADS, REC_POOL, REC_BUDGET, and RAYON_NUM_THREADS.

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How it works

The pipeline faithfully mirrors PaddleOCR's lightweight path:

1. Detection — resize (min-side 736, cap the longer side at det_max_side = 1600 by default vs PaddleOCR's 4000, round to ×32), normalize (BGR mean/std), run the DB detector. Detecting at a lower resolution locates text just as well and is much faster; recognition still crops from the full-res image.
2. DB post-process — threshold 0.2, connected components, minAreaRect, box score ≥ 0.4, unclip ratio 1.4, rescale to source coordinates.
3. Sort boxes top-to-bottom / left-to-right; crop each via perspective warp.
4. Recognition — resize each crop to H=48, normalize, batch, run the CTC recognizer ([N, T, 6906]), greedy CTC decode.
5. Reconstruct reading-order text with dynamic column/line detection.

Detection matches PaddlePaddle at 96 % IoU>0.5 with 0.93 character-level similarity on the recognized text; the residual difference is ONNX-Runtime vs PaddlePaddle floating-point numerics, not the algorithm.

The bundled models are PP-OCRv6_tiny_det and PP-OCRv6_tiny_rec exported with paddle2onnx.

Building from source

pip install maturin
maturin develop --release      # build + install into the current environment
# or
maturin build --release        # produce a wheel in target/wheels/

Requires a Rust toolchain. ONNX Runtime is fetched automatically by the ort crate at build time and linked into the extension.

Tests

cargo test --release                 # Rust unit tests (geometry, resize, CTC)
maturin develop --release            # then the Python integration tests:
python faster_paddle/tests/test_integration.py

The integration tests check the result shape, known-text detection, that the recognition session pool is deterministic, that bounds map back to original coordinates after resize, that all preprocessing options run, and a speed regression guard.

License

MIT