Project Description:

The AI Handwriting Recognition App is a web-based application that uses machine learning and OCR (Optical Character Recognition) to detect and convert handwritten text into editable, searchable digital text. It allows users to upload scanned documents, notes, or even write using a touchscreen/digital pen, and the system extracts the handwritten content with high accuracy.

This project is ideal for digitizing lecture notes, archival documents, or even recognizing numerical data from forms.

Core Objective:

To create a web app that intelligently detects, reads, and converts handwritten content into digital text using AI/ML models and image processing.

Key Features:

1. Image Upload & Live Writing:

   • Upload scanned handwritten images (JPG, PNG, PDF).

   • Optional: Use HTML5 canvas or touchscreen for live handwriting input.

2. Preprocessing of Handwritten Images:

   • Grayscale conversion, noise removal, and thresholding.

   • Line and character segmentation using OpenCV.

3. AI Model for Recognition:

   • Pre-trained CNN/RNN model or use Tesseract OCR with LSTM for handwritten text.

   • Recognizes both printed and cursive handwriting.

4. Text Output & Editor:

   • Displays recognized text in a textarea for editing or copying.

   • Supports saving the output as .txt or .pdf.

5. Web Interface:

   • Responsive UI using HTML, CSS, Bootstrap, JavaScript.

   • Real-time feedback and progress bar for recognition process.

6. Data Storage (Optional):

   • Save extracted text and original image in database.

   • Allow users to manage their scanned files and outputs.

Tech Stack:

 Backend:

• Python with Flask or Node.js

• Machine Learning model trained using TensorFlow/Keras or PyTorch

• Tesseract OCR engine with LSTM handwriting model

 Frontend:

• HTML, CSS, Bootstrap

• JavaScript for file uploads and output display

• HTML5 Canvas for live handwriting (optional)

Database (Optional):

• MySQL / MongoDB for storing user files and results