Digitalising Handwritten Hindi Documents Using Learning Models

The character classification for handwritten documents has many challenges since each individual write with different font, has a different style of writing and writes with fontsize. This unconventional style poses a challenge to traditional techniques like Optical Character Recognition (OCR) and the solutions exists at the level of recognizing the character and a word. Through this project, author extends it for entire document digitalization thus helping to preserve the important documents written in Hindi. The authors used Convolution Neural Network models to address the challenge and were able to achieve 99.19% accuracy in classifying the characters.