HANDWRITTEN DEVANAGARI CHARACTER RECOGNITION USING DEEP LEARNING - CONVOLUTIONAL NEURAL NETWORK (CNN) MODEL - Anuj Bhardwaj, Prof. (Dr.) Ravendra Singh (PalArch’s Journal of Archaeology of Egypt / Egyptology, 2020)
Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence, etc. Handwritten character recognition is the process where the machine detects and recognizes the characters from a text image and converts that processed data into a code which is understood by the machine. It is a fundamental yet challenging task in the field of pattern recognition. In this paper, we used a new public image dataset for Devnagari script character: Devnagari Character Dataset (DCD). This considered dataset consists of 92 thousand images of 46 different classes of characters of Devnagari script segmented from handwritten documents. This paper also explore the challenges in recognition of Devnagari characters. Along with the dataset, a deep learning based convolutional neural network (CNN) architecture is proposed in this paper for recognition of those handwritten characters in an unrestricted environment. Deep Convolutional Neural Network have shown superior results to traditional shallow networks in many recognition tasks. Keeping distance with the regular approach of character recognition by Deep CNN, we focus the use of Dropout and dataset increment approach to improve test accuracy. By implementing these techniques in Deep CNN, we were able to increase test accuracy by nearly 0.98 percent. The proposed architecture scored highest test accuracy of 98.13% on theconsidered dataset. The results indicate that the proposed model may be a strong candidate for handwritten character recognition and automated handwritten Devnagari script character recognition applications.