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thai-trocr

Image-to-Text
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Safetensors
Thai
English
vision-encoder-decoder
image-text-to-text
trocr
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metadata
language:
  - th
  - en
metrics:
  - cer
tags:
  - trocr
  - image-to-text
pipeline_tag: image-to-text
library_name: transformers
license: apache-2.0

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).

Document Type ThaiTrOCR EasyOCR Tesseract
Handwritten 0.190034 0.410738 1.032375
PDF Document 0.057597 0.085937 0.761595
PDF Document (EN-TH) 0.053968 0.308075 1.061107
Real Document 0.147440 0.293482 0.915707
Scene Text 0.134182 0.390583 2.408704
Adjusted Mean 0.123600 0.298474 1.269101

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.

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