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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