Ocrmnist

🚀 OCR with Hugging Face Transformers

This repository demonstrates how to perform Optical Character Recognition (OCR) using the Hugging Face Transformers library, leveraging a pretrained model for OCR on images.

🚀 Quick Start

Prerequisites

Before running the code, you need to install the required libraries. You can do this using pip:

pip install transformers
pip install pillow

Usage

You can use the provided code to perform OCR on images. Here are the basic steps:

Basic Usage

# Import the necessary libraries
from transformers import VisionEncoderDecoderModel
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests

# Load the pretrained OCR model and processor
model = VisionEncoderDecoderModel.from_pretrained("vanshp123/ocrmnist")
processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-stage1')

# Load an image for OCR. Replace "/content/left_digit_section_4.png" with the path to your image
image = Image.open("/content/left_digit_section_4.png").convert("RGB")

# Process the image using the OCR processor and generate the 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]

# generated_text will contain the text recognized from the image

Example

You can use this code as a starting point for your OCR projects. It's important to adapt it to your specific use case and customize it as needed.

📄 License

This code uses models from the Hugging Face Transformers library, and you should review their licensing and usage terms for the pretrained models.