64 lines
2.2 KiB
Markdown
64 lines
2.2 KiB
Markdown
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# Embedded OCR model (not committed)
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The handwriting/text recognition backend
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(`lib/services/ocr/onnx_recognition_backend.dart`) loads an ONNX recognition
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model and its character dictionary **from assets**:
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- `rec.onnx` — the PP-OCRv4 mobile text recognition model (CTC, input
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`3 x 48 x W`, blank class index 0).
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- `ppocr_keys_v1.txt` — the PP-OCR character dictionary, one character per line.
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Neither file is committed to the repository (the model is large and the
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dictionary is distributed with PaddleOCR). The app is built to treat their
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absence as a clean no-op: if the model or dictionary is missing, the ONNX
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backend reports unavailable and OCR falls back to the native platform backend
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(or returns nothing). Only the `.gitkeep` placeholder is committed so the
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`assets/models/ocr/` asset directory is valid at build time.
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## How to obtain and place the files
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Run the helper script on your development machine (it must download from the
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PaddleOCR sources and convert the Paddle inference model to ONNX):
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```bash
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./tool/fetch_ocr_model.sh
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```
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This places the two files here as:
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```
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assets/models/ocr/rec.onnx
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assets/models/ocr/ppocr_keys_v1.txt
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```
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### Sources
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- PP-OCRv4 mobile recognition model (PaddleOCR inference model):
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https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_infer.tar
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(English-only variant: `en_PP-OCRv4_rec_infer.tar`)
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- Character dictionary `ppocr_keys_v1.txt`:
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https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/main/ppocr/utils/ppocr_keys_v1.txt
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### Conversion
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PaddleOCR ships Paddle inference models; convert to ONNX with
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[paddle2onnx](https://github.com/PaddlePaddle/Paddle2ONNX):
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```bash
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paddle2onnx \
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--model_dir ch_PP-OCRv4_rec_infer \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--save_file rec.onnx \
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--opset_version 14 \
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--enable_onnx_checker True
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```
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## Verification note
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The backend assumes the PP-OCRv4 mobile rec convention (input `3 x 48 x W`,
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normalization `(v/255 - 0.5)/0.5`, CTC blank at index 0, dictionary shifted by
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one). If you use a different exported model, verify the input shape,
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normalization, and blank/dictionary convention and adjust
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`onnx_recognition_backend.dart` / `CtcDecoder` accordingly.
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