@inproceedings{97509035c9cd40dcba499daf348625f9,
title = "Research on document detection and recognition based on deep learning",
abstract = "As an important technology to promote office automation, document detection and recognition can improve the efficiency of business processes and user experience, make enterprise business more intelligent, and have very broad application scenarios. In this paper, a document detection and recognition system based on DB detection model and CRNN recognition model is built to detect and recognize document images using 3.64 million samples from the image dataset intercepted by ICDARD 2015 and Chinese corpus, and display the document information in the corresponding table in real time. The test results show that the system effectively improves the model inference speed while ensuring the accuracy of document recognition and detection, and completes the document information entry efficiently and quickly.",
keywords = "CRNN, Deep learning, Document identification",
author = "Yuheng Wang and Xinyan Cao and Lihua He",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; 2022 International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022 ; Conference date: 16-12-2023 Through 17-12-2023",
year = "2023",
doi = "10.1117/12.2674273",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Huang Wang",
booktitle = "International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022",
address = "United States",
}