Trsna - Smart Water Tap Extension
Arduino-based Prototype
Handwriting Recognition Model Development
Project Overview:
Developed a handwriting recognition model, achieving an accuracy of 85%. The project applied advanced concepts from Andrew Ng’s Deep Learning Specialization to address the challenges of recognizing sequential handwritten text data.
Key Achievements:
- Model Architecture: Implemented a hybrid CNN-BLSTM (Convolutional Neural Network with Bidirectional Long Short-Term Memory) architecture, combining spatial feature extraction with sequential data analysis for high accuracy.
- Advanced Loss Function: Utilized Connectionist Temporal Classification (CTC) for effective sequential data recognition, allowing the model to handle variable-length sequences without requiring pre-segmented data.
- Performance Evaluation: Achieved 85% accuracy, demonstrating the model’s capability to reliably interpret handwritten text.
Tools & Technologies:
Programming Language: Developed the model using Python, showcasing proficiency in deep learning and data processing.
Framework: Leveraged Keras for building and training the neural network, ensuring efficient prototyping and evaluation.
Impact:
Delivered a robust handwriting recognition system capable of interpreting sequential data, setting a foundation for applications in document digitization and automated text processing. This project strengthened expertise in deep learning architectures and sequential data modeling techniques.