| --- |
| 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). |
|
|
