README.md

---
license: mit
base_model: PaddlePaddle
pipeline_tag: image-to-text
library_name: onnx
language:
- multilingual
tags:
- ocr
- paddle
- onnx
---

# ONNX - Paddle

| Model | FP32 | Task |
| --- | --- | --- |
| PP-DocLayout_plus-L | pp-docLayout_plus-l.onnx | layout detection |
| PP-OCRv6_medium_det | pp-ocrV6_medium_det.onnx | text line detection |
| PP-OCRv6_medium_rec | pp-ocrV6_medium_rec.onnx | text recognition |

## Usage

```
# onnxruntime-gpu for run it on GPU
pip install onnxruntime opencv-python numpy pyclipper
```

Every model folder has the same layout:

- `src/helper.py` = onnx session builder (provider selection, threads, memory options)
- `src/example.py` = full pipeline: image preprocess, inference, postprocess
- `onnx/` = the model file

Run the commands from the repository root.

Test images used by the examples:

- [jp_1.jpg](https://github.com/cimo/One_workspace/blob/main/file/test/jp_1.jpg) = full page
- [det_box_0294.jpg](https://github.com/cimo/One_workspace/blob/main/file/test/det_box_0294.jpg) = single text line

## PP-DocLayout_plus-L

- [PP-DocLayout_plus-L/src/example.py](PP-DocLayout_plus-L/src/example.py)

```
python3 PP-DocLayout_plus-L/src/example.py jp_1.jpg
```

```
0.936555 | header | [530, 62, 1213, 104]
0.910084 | table | [1066, 176, 1692, 260]
0.891657 | footer | [1066, 1157, 1198, 1175]
0.886924 | table | [54, 395, 1694, 799]
0.827543 | text | [55, 804, 1029, 959]
```

Note:
- Input = RGB image resized to 800x800, float32 0..1, CHW with batch dimension.
- Feed = `image`, `im_shape` (800, 800), `scale_factor` (800 / height, 800 / width).
- Output row = `[classId, score, x1, y1, x2, y2]` with coordinates already in the original image space,<br>
the box count is in the second output.
- 20 label classes (header, doc_title, text, paragraph_title, image, table, chart, formula, ...):<br>
full map in [PP-DocLayout_plus-L/src/example.py](PP-DocLayout_plus-L/src/example.py).
- Boxes overlap by design (no NMS in the model): filter by score (0.3 in the example) and handle<br>
the containment on your side if needed.

## PP-OCRv6_medium_det

- [PP-OCRv6_medium_det/src/example.py](PP-OCRv6_medium_det/src/example.py)

```
python3 PP-OCRv6_medium_det/src/example.py jp_1.jpg
```

```
0.965487 | [[60, 1158], [850, 1158], [850, 1176], [60, 1176]]
0.827508 | [[1069, 1156], [1200, 1156], [1200, 1176], [1069, 1176]]
0.916008 | [[64, 1128], [837, 1128], [837, 1149], [64, 1149]]
```

Note:
- Input = BGR image (plain `cv2.imread`, no channel swap) normalized with the ImageNet mean / std,<br>
CHW with batch dimension.
- Feed = `x`, the only input, with a fully dynamic shape.
- Resize = longest side capped to 960 (4000 hard limit), then both sides rounded to a multiple of 32.
- Output = a single probability map `[1, 1, height, width]`, the DB postprocess is on your side:<br>
binarize at 0.2, `cv2.findContours`, minimum area box, score as the mean probability inside the box,<br>
keep it over 0.45, expand it with pyclipper (unclip ratio 1.4) and rescale to the original image.
- Output row = `[score, [4 corner points]]` clockwise from the top-left corner, already in the original<br>
image space. Boxes are quads, not axis aligned: a rotated line keeps its rotation.
- Crop the quads and feed them to PP-OCRv6_medium_rec to get the text.

## PP-OCRv6_medium_rec

- [PP-OCRv6_medium_rec/src/example.py](PP-OCRv6_medium_rec/src/example.py)

```
python3 PP-OCRv6_medium_rec/src/example.py det_box_0294.jpg
```

```
0.953031 | (別表十(一)「15」若しくは別表十(二)「10」又は別表十(一)「16」若しくは別表十(二)「11」)
```

Note:
- Input = a single cropped text line, not a full page: use the boxes from PP-OCRv6_medium_det.
- Input = BGR image (plain `cv2.imread`, no channel swap) resized to height 48 keeping the aspect ratio,<br>
scaled to 0..1 then normalized to -1..1, right padded with zeros, CHW with batch dimension.
- Feed = `x`, the only input, with a dynamic width (minimum 320, capped at 3200).
- Output = `[1, timeStep, 18710]` already softmaxed, decoded with a greedy CTC: drop the repeats first,<br>
then the blank at index 0.
- Character map = index 0 is the blank, 1..18708 are the lines of<br>
[PP-OCRv6_medium_rec/onnx/dictionary.txt](PP-OCRv6_medium_rec/onnx/dictionary.txt), 18709 is the space.
- Score = mean probability of the kept time steps.