thai-trocr

Thai-TrOCR Model

Introduction

ThaiTrOCR is a fine-tuned version of the TrOCR base handwritten model, specifically crafted for Optical Character Recognition (OCR) in both Thai and English. This multilingual model adeptly processes handwritten text-line images in both languages, leveraging the TrOCR architecture, which combines a Vision Transformer encoder with an Electra-based text decoder. Designed to be compact and lightweight, ThaiTrOCR is optimized for efficient deployment in resource-constrained environments while achieving high accuracy in character recognition.

• Encoder: TrOCR Base Handwritten
• Decoder: Electra Small (Trained with Thai corpus)

Training Dataset

• pythainlp/thai-wiki-dataset-v3
• pythainlp/thaigov-corpus
• Salesforce/wikitext

How to Use

Here’s how to use this model in PyTorch:

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests

# Load processor and model
processor = TrOCRProcessor.from_pretrained('openthaigpt/thai-trocr')
model = VisionEncoderDecoderModel.from_pretrained('openthaigpt/thai-trocr')

# Load an image
url = 'your_image_url_here'
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

# Process and generate text
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)

Model Performance Comparison

This section details the performance comparison between the open-source ThaiTrOCR model and other widely-used OCR systems, namely EasyOCR and Tesseract. The table below highlights their respective performance across various document types based on the average Character Error Rate (CER).

Disclaimer: The test dataset at https://huggingface.co/datasets/openthaigpt/thai-ocr-evaluation includes only 104 images, which may limit the generalizability of these results. We are increasing the number of the test dataset.

Key Insights

• Character Error Rate (CER): This metric evaluates the percentage of characters that were incorrectly predicted by the model. A lower CER indicates better performance. As shown in the table, ThaiTrOCR consistently outperforms EasyOCR and Tesseract across all document types, with a significantly lower average CER, making it the most accurate model in the comparison.
• Model Performance: The ThaiTrOCR model is particularly effective with PDF documents (both Thai-only and bilingual English-Thai texts), and shows substantial improvement over competing models in reading scene text and handwritten content.
• Tesseract Limitation: It’s important to note that Tesseract only supports single-language input at a time in this comparison. For the purposes of this benchmark, it was tested using only the Thai language setting, which might have contributed to its higher CER values.
• The evaluation dataset is sourced from the openthaigpt/thai-ocr-evaluation.

Sponsors

<img src="https://cdn-uploads.huggingface.co/production/uploads/66f6b837fbc158f2846a9108/WpQSD00FCtYjYlQXwMrDM.png" alt="Sponsors" width="500">

Authors

• Suchut Sapsathien ([email protected])
• Jillaphat Jaroenkantasima ([email protected])