---
language:
  - km
  - en
tags:
  - object-detection
  - text-detection
  - yolo
  - yolo11
  - khmer
  - ultralytics
  - pytorch
license: mit
---

# mini-text-detection — Khmer & English Text Detection

A **YOLO11n**-based text detection model fine-tuned to locate and classify text regions in images containing **Khmer and English** content.  
It detects 3 types of text blocks and can be used as the first stage before passing crops to an OCR model (e.g. [phonsobon/mini-ocr](https://huggingface.co/phonsobon/mini-ocr)).

---

## Model Details

| Property | Value |
|----------|-------|
| Architecture | YOLO11n (nano) |
| Task | Object Detection — 3 classes |
| Weights file | `khmer-text-detection-mini.pt` |
| Framework | Ultralytics / PyTorch |
| Input | RGB image, any size (auto-resized internally) |

---

## Classes

| ID | Name | Description |
|----|------|-------------|
| `0` | `subject` | Title or heading text |
| `1` | `reference` | Reference, label, or metadata text |
| `2` | `content` | Main body / paragraph text |

---

## Files

| File | Description |
|------|-------------|
| `khmer-text-detection-mini.pt` | Full Ultralytics YOLO model (weights + config) |

---

## Quick Start

### Install dependencies

```bash
pip install ultralytics huggingface_hub
```

### Run inference

```python
from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# ── Download model ────────────────────────────────────────────────────────────
model_path = hf_hub_download(
    repo_id="phonsobon/mini-text-detection",
    filename="khmer-text-detection-mini.pt",
)

# ── Class names ───────────────────────────────────────────────────────────────
CLASS_NAMES = {0: "subject", 1: "reference", 2: "content"}

# ── Load & predict ────────────────────────────────────────────────────────────
model = YOLO(model_path)

results = model.predict(
    source="your_image.jpg",   # path, URL, or numpy array
    conf=0.25,                 # confidence threshold
    iou=0.45,                  # NMS IoU threshold
    imgsz=640,
)

# ── Print results ─────────────────────────────────────────────────────────────
for r in results:
    r.show()                                        # display with bounding boxes
    for box in r.boxes:
        cls_id = int(box.cls)
        label  = CLASS_NAMES[cls_id]
        conf   = float(box.conf)
        x1, y1, x2, y2 = box.xyxy[0].tolist()
        print(f"[{label}] conf={conf:.2f}  box=({x1:.0f},{y1:.0f},{x2:.0f},{y2:.0f})")
```

### Filter by class

```python
# Get only subject (heading) boxes
subject_boxes = [b for b in results[0].boxes if int(b.cls) == 0]

# Get only content (body) boxes
content_boxes = [b for b in results[0].boxes if int(b.cls) == 2]
```

### Save annotated images

```python
results = model.predict(source="your_image.jpg", save=True, project="runs/detect")
# Saved to runs/detect/predict/
```

### Batch inference on a folder

```python
results = model.predict(source="path/to/images/", conf=0.25, imgsz=640)
for r in results:
    counts = {name: 0 for name in CLASS_NAMES.values()}
    for box in r.boxes:
        counts[CLASS_NAMES[int(box.cls)]] += 1
    print(r.path, "→", counts)
```

---

## Crop + OCR Pipeline

Combine this model with [phonsobon/mini-ocr](https://huggingface.co/phonsobon/mini-ocr) for full end-to-end document reading, with each region labelled by type:

```python
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
from PIL import Image

CLASS_NAMES = {0: "subject", 1: "reference", 2: "content"}

# ── Load detection model ──────────────────────────────────────────────────────
det_path = hf_hub_download("phonsobon/mini-text-detection", "khmer-text-detection-mini.pt")
detector = YOLO(det_path)

# ── Detect text regions ───────────────────────────────────────────────────────
image_path = "your_image.jpg"
results = detector.predict(source=image_path, conf=0.25, imgsz=640)

img = Image.open(image_path).convert("RGB")

# ── Crop each region sorted by class ─────────────────────────────────────────
for i, box in enumerate(results[0].boxes):
    cls_id        = int(box.cls)
    label         = CLASS_NAMES[cls_id]
    x1,y1,x2,y2  = map(int, box.xyxy[0].tolist())

    crop = img.crop((x1, y1, x2, y2))
    crop.save(f"crop_{i}_{label}.png")
    print(f"Saved crop {i} → class: {label}")
    # → feed each crop to phonsobon/mini-ocr for text recognition
```

---

## Input Tips

- Works on **any image size** — YOLO resizes internally to 640 px by default.
- Best results on **document photos, screenshots, and scanned pages**.
- Adjust `conf` (0.1 – 0.5) to trade recall vs. precision depending on your use case.

---

## Limitations

- May miss very small text (< ~8 px height in the original image).
- Not designed for handwritten or heavily stylised/artistic fonts.
- Performance is best on document-style layouts similar to training data.

---

## Related Model

| Model | Task |
|-------|------|
| [phonsobon/mini-ocr](https://huggingface.co/phonsobon/mini-ocr) | Text recognition (CRNN + CTC) for Khmer & English |

---

## License

MIT