DeepHandwritingRecognizer

Welcome to DeepHandwritingRecognizer, a robust offline handwritten character recognition project that employs Convolutional Neural Network (CNN) architecture. This project provides accurate recognition of handwritten characters by combining a powerful CNN model, character segmentation, and letter classification techniques.

Features

• CNN Model: Leverage the cutting-edge capabilities of a CNN architecture for precise and efficient recognition of intricate patterns in handwritten characters.

• Character Segmentation: Enhance recognition accuracy with a robust character segmentation algorithm that intelligently isolates individual characters within a handwritten word image.

• Letter Classification: Implement a classification process for each segmented letter, ensuring accurate labeling and facilitating the reconstruction of complete words.

• Offline Recognition: Designed for offline handwritten character recognition, making it versatile and accessible for various applications.

Installation

1. Clone the repository:

   https://github.com/JosephMutyaba/Handwritten-Notes-Recognition.git