Handwriting
Recognition System
Deep learning CNN model for writer identification with data augmentation and regularization. Achieved ~83.6% accuracy on unseen handwritten samples.
Interactive Demo
Upload a handwritten sample to identify the writer
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Model Performance
Training and evaluation metrics
Training Progress
Techniques Applied
Data Augmentation
Rotation, translation, zoom, flip, contrast adjustments
Regularization
Dropout, L2 weight decay, Batch Normalization
CNN Architecture
4-block convolutional network with 256-dim embedding
LR Scheduling
ReduceLROnPlateau with early stopping
About This Project
This project implements a Convolutional Neural Network (CNN) for writer identification from handwritten samples. The model learns to extract unique features from handwriting to identify who wrote a given sample.
Key achievements include:
- Designed custom CNN architecture optimized for small datasets
- Implemented comprehensive data augmentation pipeline
- Applied multiple regularization techniques to prevent overfitting
- Achieved ~83.6% accuracy on unseen handwritten samples
Technology Stack
Meet the Developer
Rahim Shah
Data Science & Machine Learning Developer
Computer Science graduate focused on data science and applied machine learning, with experience building end-to-end ML systems across computer vision, NLP, and predictive analytics. Interested in developing reliable, real-world AI solutions with attention to data quality, performance, and practical impact.