thai-ocr-model

Thai Handwritten OCR (TrOCR)

A Thai Handwritten OCR model fine-tuned from Microsoft TrOCR for recognizing Thai handwritten text.

Model Details

Model Description

This model is developed to convert Thai handwritten images into text using the TrOCR architecture, which combines Vision Transformer (ViT) for image processing and Transformer Decoder for text generation.

• Developed by: Warit Sirikosityanggoon
• Model type: Vision Encoder-Decoder (TrOCR)
• Language(s): Thai (th)
• License: Apache 2.0
• Finetuned from: microsoft/trocr-base-handwritten

Model Sources

• Repository: waritkan/Thai-Hand-Written-TrOCR-Webapp

Uses

Direct Use

This model can be used directly for converting Thai handwritten images into text. Suitable for:

• Converting Thai handwritten documents
• Real-time handwriting recognition systems
• Digitizing handwritten notes

Out-of-Scope Use

• Not suitable for languages other than Thai
• May not perform well on extremely difficult handwriting or low-quality images

Training Details

Training Data

Trained on iapp/thai_handwriting_dataset, which contains Thai handwritten images paired with their corresponding text labels.

Tokenizer

Uses SentencePiece with Unigram algorithm instead of Dictionary-based Word Segmentation because:

• Handles Out-of-Vocabulary words effectively
• Supports misspelled or incomplete words from handwriting
• No pre-tokenization required

Tokenizer Configuration:

• Vocab Size: 30,000
• Character Coverage: 0.9995
• Algorithm: Unigram

Training Hyperparameters

Training Infrastructure

• Hardware: NVIDIA GPU (HPC Cluster)
• Framework: PyTorch + Hugging Face Transformers

Evaluation

Metrics

How to Evaluate

import editdistance

def calculate_cer(pred, label):
    """Character Error Rate (lower is better)"""
    if len(label) == 0:
        return 1.0 if len(pred) > 0 else 0.0
    distance = editdistance.eval(pred, label)
    return distance / len(label)

How to Get Started with the Model

Installation

pip install transformers torch sentencepiece pillow

Usage

import torch
from PIL import Image
import sentencepiece as spm
from transformers import VisionEncoderDecoderModel, ViTImageProcessor

# Load model
model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')
image_processor = ViTImageProcessor.from_pretrained('microsoft/trocr-base-handwritten')

# Load Thai tokenizer
sp = spm.SentencePieceProcessor()
sp.Load('thai_sp_30000.model')

# Load trained weights
checkpoint = torch.load('best_model.pt', map_location='cpu')
model.decoder.resize_token_embeddings(sp.GetPieceSize())
model.load_state_dict(checkpoint['model_state_dict'], strict=False)
model.eval()

# Inference
image = Image.open('handwriting.jpg').convert('RGB')
pixel_values = image_processor(image, return_tensors='pt').pixel_values

with torch.no_grad():
    generated_ids = model.generate(
        pixel_values,
        max_length=128,
        num_beams=4,
    )

# Decode
ids = generated_ids[0].tolist()
text = sp.DecodeIds(ids)
print(text)

Model Architecture

Input Image
    |
    v
Vision Transformer (ViT) Encoder
    |
    v
Cross-Attention
    |
    v
Transformer Decoder
    |
    v
SentencePiece Tokenizer (Unigram)
    |
    v
Thai Text Output

Limitations

• Performance depends on image quality and handwriting clarity
• May not perform well on handwriting styles significantly different from training data
• Supports Thai language only

Citation

@misc{thai-handwritten-trocr,
  author = {Warit Sirikosityanggoon},
  title = {Thai Handwritten OCR using TrOCR},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://github.com/waritkan/Thai-Hand-Written-TrOCR-Webapp}}
}

Acknowledgements

• Microsoft TrOCR for Pretrained Model
• iApp Technology for Thai Handwriting Dataset
• SentencePiece for Tokenizer

Model Card Contact

• Author: Warit Sirikosityanggoon
• GitHub: waritkan/Thai-Hand-Written-TrOCR-Webapp