Research on document detection and recognition based on deep learning

Yuheng Wang; Xinyan Cao; Lihua He

doi:10.1117/12.2674273

28 April 2023 Research on document detection and recognition based on deep learning

Yuheng Wang, Xinyan Cao, Lihua He

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126260Q (2023) https://doi.org/10.1117/12.2674273
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

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.

Citation Download Citation

Yuheng Wang, Xinyan Cao, and Lihua He "Research on document detection and recognition based on deep learning", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126260Q (28 April 2023); https://doi.org/10.1117/12.2674273

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available