Printed document layout analysis and optical character recognition system based on deep learning
Dong-Lin Li, Shih-Kai Lee, Yin-Ting Liu
Abstract
This paper proposes a layout analysis and text recognition system for printed documents based on deep learning. Initially, scanned documents or image files are processed using a layout analysis algorithm based on YOLOv4 and YOLOv8 deep learning to identify the positions of titles, text paragraphs, tables, and images within the document. Each of these categories undergoes specific character segmentation processing. Then, the content is recognized using a text recognition algorithm based on Convolutional Neural Networks (CNN). Finally, the recognized text is integrated and output in editable formats, such as JSON or Microsoft formats. Our proposed method enables convenient, fast, and highly accurate OCR processing on a local computer.