An Efficient Approach for Handwritten Devanagari Character Recognition based on Artificial Neural Network
Nikita Singh · 2018
Hindi is the common and most popular language in the countries such as India, Nepal etc. People use this language not only for conversation but also in their vehicles license plates, documents, sign boards, handwritten notes etc. In recent years, many approaches have been proposed for Hindi character recognition and various applications such as text to speech translator, automatic license plate recognition etc. are proposed for these. Some computationally expensive approaches have achieved desirable accuracy but for light computing devices, recognition of handwritten characters is still challenging task. This paper proposes an approach for recognition of handwritten Devanagari character recognition. The shape variance of the character in Devanagari script is exhibited by variant of curves. These characters are distinguished using feature extraction in piecewise manner. The image partitioning technique is used for piecewise histogram of oriented gradients (HOG) features extraction. To train the neural network, a feature vector comprise of HOG features of all partitions is used. The proposed approach achieves the maximum of 99.27% classification accuracy in training and is able to recognize the different handwritten Devanagari characters with an average accuracy of 97.06%. The proposed approach may be useful in the application for blind people to read the handwritten contents.