Text Recognition Using EasyOCR

This project focuses on extracting and recognizing text from images using EasyOCR, a deep learning-based Optical Character Recognition (OCR) library that supports multiple languages and is easy to use.

Overview

The goal of this project is to demonstrate how EasyOCR can be used for efficient and accurate text recognition in images. It can be applied to various real-world scenarios such as document scanning, signboard reading, and scene text detection.

Key Features

Automatic text detection and recognition from images

Multilingual support with high accuracy

Lightweight and fast performance

Easy to integrate into larger OCR pipelines

Applications

Document digitization

Assistive technology for the visually impaired

License plate recognition

Real-time translation of text from images

Data extraction from scanned forms

Project Structure

Images – Contains the input images used for recognition

Text Results – Stores the extracted text or visual outputs

Documentation – Includes project explanations, use cases, and references

Tools & Libraries

EasyOCR

OpenCV (for preprocessing and visualization)

Python (as the programming language)

Future Enhancements

Add GUI for user interaction

Integrate with Tesseract or other OCR tools for comparison

Support batch processing of multiple images

Improve accuracy using image preprocessing techniques

License

This project is licensed under the MIT License. You are free to use, modify, and distribute it with proper attribution.