Font and Turkish Letter Recognition in Images with Deep Learning

Abstract

The purpose of this article is to recognize letter and especially font from images which are containing texts. In order to perform recognition process, primarily, the text in the image is divided into letters. Then, each letter is sended to the recognition system. Results are filtered according to vowels which are most used in Turkish texts. As a result, font of the text is obtained. In order to separate letters from text, an algorithm used which developed by us to do separation. This algorithm has been developed considering Turkish characters which has dots or accent such as i, j, ü, ö and g and helps these characters to be perceived by the system as a whole. In order to provide recognition of Turkish characters, all possibilities were created for each of these characters and the algorithm was formed accordingly. After recognizing the each character, these individual parts are sended to the pre-trained deep convolutional neural network. In addition, a data set has been created for this pre-trained network. The data set contains nearly 13 thousands of letters with 227*227*3 size have been created with different points, fonts and letters. As a result, 100 percent of success has been attained in the training. %79.08 letter and %75 of font success has been attained in the tests.