Persian Handwritten Digit, Character, and Words Recognition by Using Deep Learning Methods

Digit, character, and word recognition of a particular script play a key role in the field of pattern recognition. These days, Optical Character Recognition (OCR) systems are widely used in commercial market in various applications. In recent years, there are intensive research studies on optical character, digit, and word recognition. However, only a limited number of works are offered for numeral, character, and word recognition of Persian scripts. In this paper, we have used deep neural network and investigated different versions of DensNet models and Xception and compare our results with the state-of-the-art methods and approaches in recognizing Persian character, number, and word. Two holistic Persian handwritten datasets, HODA and Sadri, have been used. For a comparison of our proposed deep neural network with previously published research studies, the best state-of-the-art results have been considered. We used accuracy as our criteria for evaluation. For HODA dataset, we achieved 99.72% and 89.99% for digit and character, respectively. For Sadri dataset, we obtained accuracy rates of 99.72%, 98.32%, and 98.82% for digit, character, and words, respectively.