Handwritten Character Recognition
94% classification accuracy
Handwritten Character Recognition was crafted from a mission: every stroke matters, every character tells a story. Built during my Code Alpha internship, I created a system where pen meets pixel, where handwriting becomes data, and where 94% accuracy isn't just a number - it's a bridge between analog and digital.
1 / 294% classification accuracy
Multi-character support
SnapshotAt a glance
ML Engineer (Code Alpha Intern)
1.5 months
94% classification accuracy
Multi-character support
Technologies Used
4 Technologies Integrated
Impact
Key Features
Feature Implementation
Project Vision
Achieve high accuracy in handwritten character recognition.
Core Process
The process of Developing it.
Built CNN architecture, trained on handwritten character datasets, optimized for accuracy.
Build notesWhat I built
03Built a CNN-based handwritten character recognition system using TensorFlow and Keras, achieving 94% classification accuracy by implementing advanced data augmentation techniques and preprocessing methods that handle handwriting variations, creating a bridge between analog and digital text.
Developed sophisticated image preprocessing pipelines using OpenCV that normalize handwriting samples, extract meaningful features, and prepare images for classification, ensuring robust recognition across different writing styles and quality levels.
Created a multi-character support system that processes handwritten text character by character, enabling digitization of handwritten documents with high accuracy, where every stroke matters and every character tells a story through precise recognition algorithms.
Automating handwritten text recognition for digitization.
Features
- Character image classification
- CNN-based feature extraction
- Multi-character support
- High accuracy recognition
Challenges
- Handwriting variation
- Image preprocessing
- Model accuracy optimization
