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

🚀 Final Model Available Now!

The final version of the Thai-TrOCR model is out! Check it out here: huggingface.com/openthaigpt/thai-trocr

Introduction

Thai-TrOCR is an advanced Optical Character Recognition (OCR) model fine-tuned specifically for recognizing handwritten text in Thai and English. Built on the robust TrOCR architecture, this model combines a Vision Transformer encoder with an Electra-based text decoder, allowing it to effectively handle multilingual text-line images.

Designed for efficiency and accuracy, Thai-TrOCR is lightweight, making it ideal for deployment in resource-constrained environments without compromising on performance.

Key Features:

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

Training Dataset

Thai-TrOCR was trained using the following datasets:

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

How to Use This Beta Model

Here’s a quick guide to get started with the Thai-TrOCR model in PyTorch:

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

# Load processor and model
processor = TrOCRProcessor.from_pretrained('suchut/thaitrocr-base-handwritten-beta2')
model = VisionEncoderDecoderModel.from_pretrained('suchut/thaitrocr-base-handwritten-beta2')

# 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)