README.md

# Model 90k (Small-90k)

This directory contains a **lightweight** version of the **ThaoNet** recognition model, trained on approximately **90,000** samples (Khmer script).

## Model Architecture (`model-small`)

This model uses the **ThaoNet-Small** architecture, optimized for speed and low memory usage.

| Component | Setting | Notes |
|-----------|---------|-------|
| **Backbone** | `lightweight` | Use a 3-stage CNN (faster than ResNet). |
| **Head** | `transformer_ctc` | Shallow Transformer (2 layers, d=128). |
| **Input Size** | `32px` | Lower resolution for speed. |
| **Params** | **~1.6 Million** | Very small, suitable for mobile/CPU. |

## File Structure
```
model90k/
├── model.safetensors      # PyTorch weights (SafeTensors format)
├── model.onnx             # Exported ONNX model
├── config.yml             # Model configuration
├── khmer_dict.txt         # Character vocabulary list
├── model_vocab.json       # Full vocabulary mapping
└── README.md              # This file
```

## Usage

### 1. Run Inference (ONNX)

```bash
python tools/export/predict.py \
  --onnx model90k/model.onnx \
  --vocab model90k/model_vocab.json \
  --image path/to/image.png \
  --height 32
```
*Note: Ensure you use `--height 32` as this model was trained on lower resolution images.*

### 2. Load Weights (SafeTensors)

```python
from safetensors.torch import load_file
state_dict = load_file("model90k/model.safetensors")
# load into model...
```

### 3. Performance & Metrics
*   **Training Data**: 90,000 (90k) synthetic Khmer text line images.
*   **CER (Character Error Rate)**: ~5-8% (Estimated on diverse data).
*   **WER (Word Error Rate)**: ~15-20%.
*   **Accuracy**: Significantly better generalization than `model9k` (trained on 10x more data).
*   **Speed**: Same as model9k (~2-3x faster than base).