Author : Prashant Misal, Prof.A.J.Patankar and Dr. Nitika Singhi

A normal human can easily recognize any written or typed or scanned text, numbers, etc., but when it comes to a machine, it is difficult to find out what exactly that given text or numbers. It will be difficult to recognize a handwritten digit for a machine. Many machine learning methods were used to fix the handwritten digit recognition issue. It is growing in more convoluted domains, so its training complexity is also increasing. To overcome this complexity problem, many algorithms have been implemented. In this project, the Convolutional Neural Network (CNN) with transfer learning and dropout methods, these approaches do use for recognition of the isolated handwritten alphabets. Transfer learning and dropouts are used to reduce the overall computation time of the proposed system. The customized Transfer learning and dropout techniques with CNN, to decreases the required number of epochs for training. It is used to identify Marathi Characters in the Devnagari handwritten digital database to predict the Scripts. We will try to achieve Maximum accuracy in short time.

Keywords: Handwritten Character Recognition, Convolutional Neural Network;