OCR

easy ocr

check lang codes at https://www.jaided.ai/easyocr/

other OCR with GPU

If you’re looking for open-source AI-based OCR solutions that can leverage your NVIDIA GPU and process Traditional Chinese (zh-TW), here are some excellent options:

1. Tesseract OCR with GPU Support

• Description: Tesseract is a well-established open-source OCR engine that supports multiple languages, including Traditional Chinese. However, it doesn’t natively support GPU acceleration, but you can pair it with pre-processing tools like OpenCV or other AI models to boost performance.
• Key Features:
  • High customization and language support (including Traditional Chinese).
  • Works well for clean, printed text.
• Limitations:
  • Relatively slow compared to modern AI-based OCR solutions.
• Setup:
  • Install tesseract-ocr and the Traditional Chinese language data package (chi_tra).
  • Can be used with Python via the pytesseract library.
• GPU Option:
  • Pre-process images using GPU-accelerated libraries like OpenCV with CUDA.

2. EasyOCR

• Description: EasyOCR is a modern, AI-powered OCR library written in PyTorch. It supports GPU acceleration out of the box and handles Traditional Chinese well.
• Key Features:
  • Multilingual support, including zh-TW.
  • Lightweight and easy to set up.
  • Can leverage NVIDIA GPUs for faster processing.
• Setup:
  1. Install via pip: pip install easyocr.
  2. Run the code:

     import easyocr
     reader = easyocr.Reader(['zh-tw'], gpu=True)
     result = reader.readtext('path_to_image')
     

• Limitations:
  • Struggles with very complex or heavily distorted handwriting.

3. PaddleOCR

• Description: PaddleOCR is a powerful OCR tool developed by Baidu. It supports GPU acceleration using NVIDIA GPUs and provides excellent accuracy, especially for Chinese text.
• Key Features:
  • Optimized for Chinese languages.
  • High accuracy for both printed and handwritten text.
  • Built-in tools for image pre-processing and text detection.
• Setup:
  1. Install the PaddleOCR package:

     pip install paddleocr
     pip install paddlepaddle-gpu  # Ensure GPU support
     

  2. Use the library:

     from paddleocr import PaddleOCR
     ocr = PaddleOCR(use_gpu=True, lang='ch')
     result = ocr.ocr('path_to_image', cls=True)
     

• Limitations:
  • Requires installing PaddlePaddle, which can have specific system requirements.

4. OCR with OpenCV and Deep Learning Models

• Description: OpenCV allows integration with custom deep learning OCR models like CRNN (Convolutional Recurrent Neural Network) or SAR (Sequence-to-Sequence Attention-based OCR). These models can be trained or fine-tuned on Traditional Chinese datasets.
• Key Features:
  • Customizable for your specific needs.
  • Full GPU acceleration using NVIDIA CUDA.
• Setup:
  • Use OpenCV with CUDA for pre-processing (e.g., noise removal, binarization).
  • Combine with a deep learning framework (e.g., PyTorch or TensorFlow) for OCR.

5. TrOCR by Microsoft

• Description: TrOCR is a transformer-based OCR model provided by Microsoft. It supports multilingual text recognition, including Chinese, and works efficiently with GPU acceleration.
• Key Features:
  • State-of-the-art accuracy.
  • Uses transformers for improved contextual understanding.
• Setup:
  1. Install the transformers library:

     pip install transformers
     

  2. Use the model:

     from transformers import TrOCRProcessor, VisionEncoderDecoderModel
     from PIL import Image
     import torch
     
     processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
     model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten").cuda()
     
     image = Image.open('path_to_image').convert("RGB")
     pixel_values = processor(images=image, return_tensors="pt").pixel_values.cuda()
     generated_ids = model.generate(pixel_values)
     text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
     print(text)
     

• Limitations:
  • Requires fine-tuning for best performance on Traditional Chinese.