Enhancing Study Experience using Handwritten Character and Digit Recognition and Text Summarization

Enhancing Study Experience using Handwritten Character and Digit Recognition and Text Summarization

Franklin Robert Viegas, Anjali Arvind Bodke, Paridhi Sudhir Wasnik, Avinash Manojan Choran

Department of Computer Engineering PVPIT

---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - The integration of Handwritten characters and, Digit Recognition and Deep Learning in education heralds a transformative era in learning methodologies. This abstract delves into the multifaceted benefits derived from the amalgamation of these technologies, redefining the educational landscape. Handwritten characters and Digit Recognition technology facilitates the seamless digitization of handwritten content, transcending the limitations of manual note-taking. Its introduction into educational frameworks enhances accessibility, promotes organization, and augments the searchability of diverse educational materials. Deep Learning, acting in tandem with Handwriting Recognition, amplifies these advantages manifold. Deep learning powered study assistants offer personalized learning experiences tailored to individual needs, adapting to varied learning styles. Additionally, these assistants facilitate collaborative opportunities, providing real time feedback and evaluation tools that revolutionize the learning process.

Key Words: Image recognition, CNN, RNN, LSTM, Neural Networks, SVM, Deep Learning, Convolutional layer, PreLU, ReLU, Text Summarization, Extractive text summarization, Tokenization.