AI Handwritten Text Recognition | Deep Learning OCR Project
🚀 Build an AI-powered Handwritten Character Recognition system that converts noisy handwritten text images into digital text using Deep Learning!
In this project, I'll show you how to create a real-time OCR (Optical Character Recognition) application using:
✅ TensorFlow/Keras for Deep Learning
✅ CNN (Convolutional Neural Networks) for feature extraction
✅ Bidirectional LSTM for sequence recognition
✅ CTC Loss for text alignment
✅ Streamlit for beautiful web interface
✅ OpenCV for image preprocessing
📋 What You'll Learn:
How to build a CNN + BiLSTM architecture for text recognition
Image preprocessing techniques for noisy handwritten text
CTC decoding for sequence-to-sequence recognition
Creating an interactive web app with Streamlit
Deploying a production-ready OCR system
🎯 Key Features:
Handles noisy and imperfect handwritten text
Real-time text recognition
Supports A-Z, spaces, hyphens, and apostrophes
High accuracy on diverse handwriting styles
Privacy-first: all processing happens locally
💻 Tech Stack:
Python 3.x
TensorFlow 2.13+
Streamlit
OpenCV
NumPy, PIL
📁 Project Files:
Complete source code
Trained model
Dataset preparation guide
Step-by-step implementation
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