---
language:
- km
tags:
- ocr
- khmer
- text-recognition
- image-to-text
library_name: pytorch
pipeline_tag: image-to-text
---

# Netra OCR V2 — Khmer Text Recognition (Phase A, in progress)

Khmer text recognizer over character-cluster (KCC) tokens, based on
*Towards Universal Khmer Text Recognition* (Kong et al.) and extended to address
both limitations raised in the paper's §6: a **supervised MAFS modality router**
and a **learned decoder router**.

Code: https://github.com/NDarayut/Netra-OCR-V2

## ⚠️ Status — this is not a finished model

`phase_a_epoch26.pt` is a **mid-training snapshot**: Phase A (document/printed
pretraining) at epoch 26, and **Phase B (scene & handwritten adaptation) has not
been run**. It is published for checkpointing and reproducibility, not for use.

**No accuracy numbers are claimed.** The model has not been evaluated on the
held-out test sets, and it has seen essentially no scene-text or handwritten
data yet — expect it to perform poorly outside clean printed documents. Do not
deploy this for anything that matters.

## Architecture

```
image → CBAM-refined VGG backbone → Transformer encoder → MAFS → ┬→ AR decoder  (accurate)
                                                                 └→ CTC head    (fast)
```

- 22.6M parameters, float32.
- Input height 48px, chunked at width 100 with 16px overlap.
- Trained jointly with `l_Total = l_CTC + l_TR`; the AR decoder uses a 30%
  input-token masking regularizer.
- MAFS router: 5 slots, first 3 supervised against per-source document / scene /
  handwritten labels.

## Files

| File | Contents |
|---|---|
| `phase_a_epoch26.pt` | `{"model": state_dict, "epoch": 26, "phase": "a"}` — inference weights only |

Optimizer and scheduler state are **not** included, so this checkpoint cannot be
used to resume training.

## Usage

```python
import torch
from netra_ocr_v2.config import OCRConfig
from netra_ocr_v2.model import KhmerOCR

ck = torch.load("phase_a_epoch26.pt", map_location="cpu")
model = KhmerOCR(OCRConfig())
model.load_state_dict(ck["model"])
model.eval()
```

See the [repo](https://github.com/NDarayut/Netra-OCR-V2) for preprocessing and
the predictor.

## Training data

Ten Khmer OCR sources from the Hub — the work of their respective authors,
including datasets from `SoyVitou`, `seanghay`, `KrorngAI`, `LazyGreed`,
`KiteAether`, plus Google Research's HierText. The full split with modality
labels is in `dataset_split.md` in the repo. Please credit those authors if you
build on this.

## Citation

Base architecture: Kong et al., *Towards Universal Khmer Text Recognition*.
Also builds on CBAM (Woo et al. 2018), Mask-Predict (Ghazvininejad et al. 2019),
and blockwise parallel decoding (Stern et al. 2018).