From machine generated to handwritten character recognition; a deep learning approach
Kian Peymani, Mohsen Soryani · 2017
While the task of Optical Character Recognition is deemed to be a solved problem in many languages, it still requires certain improvements in some languages with more complex script structures such as Farsi. Furthermore, Deep Convolution Neural Networks have reached excellent results in various computer vision tasks, including character recognition. Although, these networks require a great amount of data to be properly learned and (in some cases) lack generalization. In order to address this issue, in this work, we propose a tailored dataset and a delicately designed model that can be trained on only machine-generated character images with various typefaces and not only achieve an excellent result on machine generated images, but also achieve a decent accuracy in detecting handwritten characters.