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Refactor: migrate pdf_parser.py to golang (#16323)
### What problem does this PR solve? Http API based on onnx model. pdf_parser.py to golang ### Type of change - [x] Refactoring
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Jack
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deepdoc/server/README.md
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deepdoc/server/README.md
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# OSS DeepDoc HTTP API Service
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Serves DLA (Document Layout Analysis), OCR (Optical Character Recognition), and
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TSR (Table Structure Recognition) models via a unified HTTP API using
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[LitServe](https://github.com/Lightning-AI/litserve) and OSS ONNX Runtime models.
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## Quick Start
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```bash
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# Build
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docker build -f Dockerfile_deepdoc_oss -t deepdoc_oss:latest .
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# Run (CPU only; no GPU required)
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docker run -p 9390:9390 deepdoc_oss:latest
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# Or via docker compose
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docker compose -f docker/docker-compose.yml up -d
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```
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The service listens on port **9390** by default. Pass `--port` to change it:
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```bash
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python deepdoc/server/deepdoc_server.py --port 9000 --model-dir /path/to/models
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```
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## Endpoints
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All prediction endpoints accept JPEG images via `multipart/form-data`. The form
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field for file uploads is named `request`.
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| Method | Path | Description |
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|--------|------|-------------|
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| `GET` | `/health` | Liveness probe. Returns `ok`. |
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| `GET` | `/model` | Model metadata. Returns `{"model":"oss","version":"1.0"}`. |
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| `POST` | `/predict/dla` | Document Layout Analysis. |
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| `POST` | `/predict/tsr` | Table Structure Recognition. |
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| `POST` | `/predict/ocr` | OCR — use form field `operator=det` for detection or `operator=rec` for recognition. |
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### `POST /predict/dla`
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Analyzes a full page image and returns labelled layout regions.
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**Request**
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```
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curl -X POST http://localhost:9390/predict/dla \
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-F "request=@page.jpg;type=image/jpeg"
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```
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**Response**
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```json
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{
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"bboxes": [
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[x0, y0, x1, y1, score, class_id],
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...
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]
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}
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```
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| class_id | Label |
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|:--------:|-------|
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| 0 | title |
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| 1 | text |
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| 2 | reference |
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| 3 | figure |
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| 4 | figure caption |
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| 5 | table |
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| 6 | table caption |
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| 8 | equation |
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> The OSS model uses 8 unique class IDs. IDs 7 and 9 are reserved for
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> compatibility with the SaaS label scheme but are never produced by the
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> OSS model.
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### `POST /predict/tsr`
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Recognizes table structure from a cropped table image.
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**Request**
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```
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curl -X POST http://localhost:9390/predict/tsr \
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-F "request=@table_crop.jpg;type=image/jpeg"
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```
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**Response**
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```json
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{
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"bboxes": [
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[x0, y0, x1, y1, score, class_id],
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...
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]
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}
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```
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| class_id | Label |
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|:--------:|-------|
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| 0 | table |
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| 1 | table column |
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| 2 | table row |
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| 3 | table column header |
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| 4 | table projected row header |
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| 5 | table spanning cell |
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### `POST /predict/ocr`
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Two modes controlled by the `operator` form field.
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#### Detection (`operator=det`)
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Returns quadrilateral bounding boxes for detected text regions.
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```
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curl -X POST "http://localhost:9390/predict/ocr" \
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-F "operator=det" \
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-F "request=@page.jpg;type=image/jpeg"
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```
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**Response** (5-level nested array):
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```json
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{
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"output": [
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[
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[
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[
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[[x0,y0],[x1,y1],[x2,y2],[x3,y3]],
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...
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]
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]
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]
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]
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}
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```
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#### Recognition (`operator=rec`)
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Recognizes text within a cropped region.
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```
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curl -X POST "http://localhost:9390/predict/ocr" \
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-F "operator=rec" \
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-F "request=@char_crop.jpg;type=image/jpeg"
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```
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**Response** (4-level nested array):
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```json
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{
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"output": [
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[
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[
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["recognized text", 1.0],
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...
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]
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]
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]
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}
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```
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> Confidence is always `1.0` — the OSS recognition model does not return
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> per-character confidence scores.
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## Error Responses
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| Scenario | HTTP Status |
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|----------|:-----------:|
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| Missing `operator` field (OCR) | 400 |
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| Invalid `operator` value | 400 |
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| Empty or corrupt image | 400 |
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| Image exceeds 4096×4096 | 400 |
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| Internal inference error | 500 |
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## Models
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All ONNX models are from the [InfiniFlow/deepdoc](https://huggingface.co/InfiniFlow/deepdoc)
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HuggingFace repository (Apache 2.0 license):
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| File | Size | Purpose |
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|------|------|---------|
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| `layout.onnx` | 75.7 MB | DLA (YOLOv10) |
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| `det.onnx` | 4.7 MB | OCR text detection (PP-OCRv4) |
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| `rec.onnx` | 10.8 MB | OCR text recognition (PP-OCRv4) |
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| `tsr.onnx` | 12.2 MB | TSR (PaddleDetection) |
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| `ocr.res` | 26 KB | OCR character dictionary |
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## Architecture
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```
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deepdoc/server/
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├── deepdoc_server.py # LitServe entry point
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├── endpoints/ # LitAPI endpoints (HTTP layer)
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│ ├── dla_endpoint.py
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│ ├── tsr_endpoint.py
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│ └── ocr_endpoint.py
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└── adapters/ # Model wrappers (inference + format conversion)
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├── dla_adapter.py
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├── tsr_adapter.py
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└── ocr_adapter.py
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```
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Endpoints → Adapters → `deepdoc/vision/` (reused OSS model classes) → ONNX Runtime.
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