faster-paddle 0.0.5

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.

• ⚡ ~7× faster than paddleocr on CPU for the same models and output.
• 📦 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)    3.0 s / image     →  ~7.7× faster

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

---

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, rec_batch=6)

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.

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)	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.

• 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 size (default 6).

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

---

How it works

The pipeline faithfully mirrors PaddleOCR's lightweight path:

1. Detection — resize (min-side 736, clamp max-side 4000, round to ×32), normalize (BGR mean/std), run the DB detector.
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.

License

MIT