--- license: mit tags: - ocr - handwritten-text - trocr - pytorch --- # Model Name: TrOCR Fine-Tuned on Custom Dataset This model is a fine-tuned version of Microsoft's `TrOCR` on a custom dataset for handwritten text extraction from scanned documents. ## 🧠 Model Architecture - **Base model**: Microsoft TrOCR (base) - **Used with**: CRAFT for text detection - **Fine-tuned with**: OCR-specific dataset ## 📁 Files in this repository: - `pytorch_model.bin`: Model weights (2.1 GB) - `config.json`, `tokenizer_config.json`, etc. - Training and evaluation scripts (optional) ## 🚀 How to Use ```python from transformers import VisionEncoderDecoderModel, TrOCRProcessor from PIL import Image import torch # Load processor and model processor = TrOCRProcessor.from_pretrained("Gitesh2003/MESA_TrOCR") model = VisionEncoderDecoderModel.from_pretrained("Gitesh2003/MESA_TrOCR") # Load image image = Image.open("sample_image.jpg").convert("RGB") # OCR 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)