Bangla Font Recognition using Transfer Learning Method

Abstract

Font detection and similar font suggestions are essential in computer vision, document analysis, pattern recognition, web development, and core work for graphics designers and UX-UI engineers. Even though Bangla is one of the most communicated languages globally and the growing popularity of Bangla in online publications and social media platforms, there has been a noticeable demand for Bangla fonts. However, there is no appreciable work for font detection in Bangla, unlike other high resource languages like English, Chinese, Hindi, Arabic, Spanish, and German. This work represents a model to recognize Bangla fonts from images using the transfer learning method. In Image Processing and Classification, resources are insufficient for such work in Bangla, so it is required to build as much raw data as possible to train the model. Therefore, as part of the work, 6500 raw images of five different fonts are used, and with augmentation 26000 image data are created to train and 2600 images to validate the model. Three transfer learning models, which are VGG-16, VGG-19, and Xception are applied. Among them, VGG-16 archives the highest accuracy of 96.23%. This paper is the first publicly available work on Bangla font recognition using the Transfer Learning approach to the best of our knowledge.