Deep Learning based Ancient Literature Recognition and Preservation

This paper introduces an deep learning based ancient literature recognition method for culture heritage preservation by deep learning. The model of Single Shot Multibox Detector is used for detecting and recognizing the ancient characters. This paper only focuses on the recognition of Oracle Bone In...

Full description

Saved in:
Bibliographic Details
Published in2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) pp. 473 - 476
Main Authors Meng, Lin, Kamitoku, Naoto, Kong, Xiangbo, Yamazaki, Katsuhiro
Format Conference Proceeding
LanguageEnglish
Published The Society of Instrument and Control Engineers - SICE 01.09.2019
Subjects
Ancient Characters Recognition
Bones
Character recognition
Computational modeling
Deep learning
Detectors
Feature extraction
Image recognition
Single Shot Multibox Detector
Online AccessGet full text

Cover

More Information
Summary:This paper introduces an deep learning based ancient literature recognition method for culture heritage preservation by deep learning. The model of Single Shot Multibox Detector is used for detecting and recognizing the ancient characters. This paper only focuses on the recognition of Oracle Bone Inscriptions which is the one of the oldest and most mysterious ancient characters, used about 3000 years ago in china. The experimental results show that Precision achieves and Recall achieves 0.86 and 0.97, respectively, and prove the effectiveness of Single Shot Multibox Detector in ancient characters recognition. By analyzing the experimental results, we found that in the case of heavy blurred image or tilted characters, the Single Shot Multibox Detector can not achieve a exciting results. Hence, we apply the pre-processing of binarization, changing brightness and contrast, and rotation in the test images, and then achieve accuracy improvement of recognition by using pre-processed images.
DOI:10.23919/SICE.2019.8860070