Abstract:

This study presents a comprehensive review of OCR (optical character recognition), Translation, and Object Detection Research from a single image. With the fast advancement of deep learning, more powerful tools that can learn semantic, high-level, and deeper features have been proposed to solve the issues that plague traditional systems. The rise of high-powered desktop computer has aided OCR reading technology by permitting the creation of more sophisticated recognition software that can read a range of common printed typefaces and handwritten texts. However, implementing an OCR that works in all feasible scenarios and produces extremely accurate results remains a difficult process. Object detection is also the difficult problem of detecting various items in photographs. Object identification using deep learning is a popular use of the technology, which is distinguished by its superior feature learning and representation capabilities when compared to standard object detection approaches. The major focus of this review paper is on text recognition, object detection, and translation from an image-based input application employing OCR and the YOLO technique.

Description:

Text recognition, Optical character recognition, Object detection, Language translation, YOLO