Comprehensive study of Deep learning based Telugu OCR
Muni Sekhar Velpuru, Priyadarshini Chatterjee, G Tejasree, Manish Kumar, S Nageswara Rao · 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) · 2020
Optical Character Recognition (OCR) is a technology to recognize text in the images. Images in the sense means scanned documents or pictures. With the help of an OCR, it is possible to convert any image text into machine-readable text data. OCR has varied uses in understanding any scripts written in any language. They also convert the text with high accuracy. After any image text has gone through OCR processing, they edited with the help of MS Office. With the advent of OCR, there is no need to digitize the image text. Another advanced technology that is extensively used in OCR is deep earning. Deep learning can be called as a subfield of Machine Learning. This area is inspired by the structure and function of the brain. Deep learning has many algorithms that are used to solve problems. This area offers scalable solutions to problems. Another very important benefit of deep learning is feature extraction from unorganized data. In a broader sense, this area provides automated feature learning. It is called “deep”, as the layers of the network are made to learn deep. As segmentation is a part of OCR, delve into it in this paper. Segmentation is a subpart of image processing; OCR and it goes hand in hand with deep learning. Segmentation helps in easier analysis of the parts of an image. Nowadays segmentation is clubbed with deep learning and artificial intelligence to analyze and understand those features that was not possible attest a decade ago. Deep learning helps to learn the input patterns so that they can predict object classes. Image segmentation divides the input image into segments to help us to do a better analysis of the image.