Results and Discussion: The system is tested thoroughly using synthetic and degraded data. Materials and Methods: In our experiments, text typeset with three English fonts (Comic Sans MS, DejaVu Sans Condensed, Times New Roman) have been used. Because font recognition works in conjunction with other methods like Optical Character Recognition (OCR), we used Decapod and OCRopus software as a framework to present the method. The system is based on the Eigenfaces method. Introduction: In this paper, a system for recognizing fonts has been designed and implemented. The proposed work demonstrated outstanding performance, even with few training samples, compared to other related works for Arabic calligraphy recognition. The results confirmed the outperformance of both individual and combine features coded by our descriptor. Three scenarios have been considered in the experimental part to prove the effectiveness of the proposed tool. These indices were transformed into a descriptor that defines, for each calligraphy style, a set of specific features. To this end, we were inspired by the indices used by human experts to distinguish different calligraphy styles. The present work aims to identify Arabic calligraphy style (ACS) from images where text images are captured by different tools from different resources. Thus, we propose a new computational tool for Arabic calligraphy style recognition (ACSR). TensorFlow is used for testing the accuracy and success rate.ĭespite the importance of recognizing Arabic calligraphy styles and their potential usefulness for many applications, a very limited number of Arabic calligraphy style recognition works have been established. As the result, 85% Tamil letters are recognized and 82% are tested using font. 15 thousands of letters are taken and 512 X 512 X 3 size deep convolution network is created with font and letters. The two dataset are created which contains test data with Tamil letter and second one for recognized input data or trained data. The trained data are applied into deep convolution neural network process. For example, Tamil letters such as are available in test dataset.
The trained data are input which contains filtered letter from image. The Tamil letters are test data and loaded in recognition systems. The filtering system is used for identifying font based on that letters are found. Our proposed algorithm is used to separate letter from the text using convolution neural network approach. Each recognized letters are sending to recognition system and filter the text using deep learning algorithms. This is recognition process, the text in the images are divided to letter or characters.
This paper proposes a deep learning approach to recognize Tamil Letter from images which contains text.
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