---
license: mit
library_name: dbnet-text-detector
pipeline_tag: object-detection
tags:
  - text-detection
  - dbnet
  - khmer
  - ocr
  - onnx
---

# DBNet Khmer Text Detector

A [DBNet](https://arxiv.org/abs/1911.08947) (Differentiable Binarization) text **detection**
model for Khmer/English documents — MobileNetV3 backbone + FPN + DBHead. Detects bounding
boxes around text lines; it does not perform OCR/recognition.

Trained from scratch on synthetic Cambodian-style documents (plain docs, ID cards, official
letters). See [dbnet-text-detector](https://github.com/netra-ai-lab/DBNet-Text-Detection) for
the full training pipeline, data generators, and inference code.

> **2026-07-31 — the default weights changed.** `khmer_dbnet_mobilenetv3.onnx` now serves
> **v2**, a fine-tune on realistic ID-card/official-letter documents. It detects noticeably
> more text on real photographed documents than v1 did. The previous weights remain available
> unchanged as `khmer_dbnet_mobilenetv3_v1.onnx` — pin that file if you need the old behaviour.

## Files

| File | Description |
|---|---|
| `khmer_dbnet_mobilenetv3.onnx` | **Default.** Currently identical to v2. Recommended for inference (torch-free, via `onnxruntime`) |
| `khmer_dbnet_mobilenetv3.pth` | PyTorch checkpoint of the default — for fine-tuning or `DBNet.pytorch`-based inference |
| `khmer_dbnet_mobilenetv3_v2.onnx` / `.pth` | v2 explicitly, pinned |
| `khmer_dbnet_mobilenetv3_v1.onnx` / `.pth` | v1 explicitly, pinned — the original release |

## Usage

```bash
pip install dbnet-text-detector
```

```python
from dbnet_detector import DBNetDetector

detector = DBNetDetector.from_pretrained()          # current default (v2)
boxes, scores = detector.detect("your_image.jpg")

detector = DBNetDetector.from_pretrained("v1")      # pin an older version
detector = DBNetDetector.from_pretrained(preset="display")   # large display text
```

Weights are downloaded from this repo on first use and cached locally
(`~/.cache/huggingface/hub`).

### Thresholds matter more than you'd expect

The library defaults to the `document` preset (`box_thresh=0.3`, `short_size=1088`), which is
tuned for dense scanned or photographed pages. The older `box_thresh=0.6, short_size=736`
values silently dropped whole text lines on real documents — on one sample page, 24 detected
lines versus 79 with the current defaults.

Large display text (posters, captions over photos) is the opposite case and needs
**down**scaling — use `preset="display"` (`short_size=320`). The model was trained on small
text lines inside 640px crops, so very large glyphs fall outside its training scale.

## Versions & metrics

| Version | Training | Val F1 |
|---|---|---|
| v1 | from scratch on plain synthetic docs | 0.987 *(v1 val set)* |
| **v2** *(default)* | v1 fine-tuned on synthetic ID cards / official letters | 0.933 *(v2 val set)* |

⚠️ **These two numbers are not comparable and do not rank the models.** Each is measured on its
own in-distribution synthetic validation set, so each says "this model learned its own data" —
not that v1 is better. On real photographed documents v2 detects substantially more text,
which is what the fine-tune was for. Both figures are synthetic and in-distribution; neither
is evidence of generalization to arbitrary real-world photographs. Validate on your own images.

## License

MIT