README.md

---
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](https://huggingface.co/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**:

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

# Load processor and model
processor = TrOCRProcessor.from_pretrained('suchut/thaitrocr-base-handwritten-beta3')
model = VisionEncoderDecoderModel.from_pretrained('suchut/thaitrocr-base-handwritten-beta3')

# 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)
```