Realtime Detection of Table Objects and Text Areas for OCR Preprocessing

Abstract

OCR (Optical Character Recognition) is a technology that automatically detects, recognizes, and digitally converts text into images. OCR has a variety of uses, including reducing human error when viewing and typing documents and helping people work more efficiently with documents. It can increase efficiency and save money by eliminating the need to manually type text, especially when scanning documents or digitizing images. OCR is divided into text object detection and text recognition in an image, and preprocessing techniques are used during the original document imaging process to increase the accuracy of OCR results. There are various preprocessing techniques. They are generally classified into image enhancement, binarization techniques, text alignment and correction, and segmentation techniques. In this paper, we propose a special-purpose preprocessing technique and application called Table Area Detection. Recently, table detection using deep learning has been actively researched, and the research results are helping to improve the performance of table recognition technology. Table detection will become an important preprocessing technology for text extraction and analysis in various documents, and it requires a lot of research and accuracy. While many previous studies have focused on improving the accuracy of OCR algorithms through various techniques, this study proposes a method to discover and exclude false positives by introducing a factor called Table Area Detection.