siemreap-arial-khmer-ocr

Khmer OCR - Siemreap/Arial CRNN Models

This repository contains Khmer OCR text-line recognition checkpoints trained on the 200k synthetic Siemreap/Arial OCR dataset.

The models recognize rendered Khmer text-line images using a CRNN pipeline:

Input image -> Modified ResNet CNN -> vertical mean pooling -> 2-layer BiGRU -> linear classifier -> CTC decoding

Available Checkpoints

Dataset

The models were trained on a generated 200,000-image Khmer OCR text-line dataset using:

• Khmer font: Siemreap
• Latin fallback font: Arial
• Image height: 64 px
• Image width: dynamic
• Train/validation/test split: 160k / 20k / 20k

Text sources:

• Approximately 100,000 Hanuman-derived Khmer lines from seanghay/khmer-hanuman-100k. Credit to the original uploader/owner seanghay.
• Approximately 100,000 contextual Khmer Wikipedia / word-Markov generated lines.

Dataset repo:

KimkosalYon/khmer-ocr-200k-siemreap-arial

Architecture Details

Both models use the same OCR head and training pipeline. The only main difference is the ResNet backbone depth.

Shared Pipeline

Input Image: (B, 1, H, W)
        ↓
Modified ResNet18/34 backbone
        ↓
Feature Map: (B, 512, H', W')
        ↓
Vertical Mean Pooling: (B, 512, W')
        ↓
Sequence Formatting: (T, B, 512)
        ↓
2-layer BiGRU, hidden size 256, dropout 0.2
        ↓
Linear classifier: 193 logits
        ↓
CTC decoding / loss

OCR ResNet Modification

The ResNet backbone is adapted for OCR by:

• changing the first convolution to grayscale input: Conv2d(1, 64, 7, 2, 3)
• changing later ResNet strides from (2,2) to (2,1) in layers 3 and 4
• preserving horizontal resolution so CTC has enough time steps for Khmer text

Training

• Epochs: 25
• Batch size: 256
• Image height: 64
• Optimizer: AdamW
• Loss: CTC loss, blank index 0
• Augmentation: random rotation, brightness/contrast jitter, Gaussian blur, random erasing

Loading

The checkpoint metadata contains the character vocabulary mapping c2i. The vocab.json file is also uploaded for convenience.

For ResNet34, use the KhmerCRNN_BiGRU model class from the project source.

For ResNet18, use the same architecture pattern but initialize the CNN backbone with torchvision.models.resnet18(weights=None) instead of ResNet34.

Limitations

These models were trained on synthetic rendered text-line images. Real scanned documents, photographed text, handwriting, unusual fonts, heavy blur, and severe perspective distortion may require additional fine-tuning.

Credits

Dataset construction uses text derived partly from seanghay/khmer-hanuman-100k. Please credit the original owner/uploader seanghay when referencing the Hanuman-derived source text.