Chronological Evolution: Development and Identification of an Odia Handwritten Character Dataset Using Deep Learning

Abstract

There is a level of saturation obtained about Online or Printed character recognition about Indian script. Comparing the research of different Indian languages obtained yet a few researches has been done for Odia characters. There are still more research remains valid while recognizing handwritten Odia script. To overcome this challenges a proper dataset is required for experimentation. The handwritten data collected from various Odia writers. There are four process carried out to generate dataset i.e. data collection, Pre-processing, Segmentation and classification. Data is collected from different sources like school and college students, village people etc. arranged them into different age groups, gender and education level. In pre-processing selection of image, noise removal, normalization, conversation of grey scale to binary image, then converted binary images to inverted images. In this paper we have focused only handwritten simple characters that included vowels, consonants, numbers and special characters. We have created 95000 of character data of different varieties compared with other availability dataset and to find its better accuracy we have used `deep learning approach like InceptionV3 with different batch size and epoch level and find its accuracy. We obtain an accuracy of 84.50 for the used model for our collected dataset. Our handwritten data is available publically and it will be useful for the researcher to continue their future research work and for the time being it can be available on request.