Qianting Zhou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Qianting Zhou has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Xuanming Fu
East China Normal University
1 shared publication - Yidan Zhang
Software Engineering Institute, East China Normal University, Shanghai 200062, China
1 shared publication - Zhenbing Zeng
Shandong Xiehe University, Jinan, Shandong, China
1 shared publication - Zhengfeng Yang
East China Normal University, Shanghai, Shanghai, China
1 shared publication
Latest Publications
- Open Access
- 9 January 2022
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 11 (1) , 45
Improvement of Oracle Bone Inscription Recognition Accuracy: A Deep Learning Perspective
Deep learning techniques have been successfully applied in handwriting recognition. Oracle bone inscriptions (OBI) are the earliest hieroglyphs in China and valuable resources for studying the etymology of Chinese characters. OBI are of important historical and cultural value in China; thus, textual research surrounding the characters of OBI is a huge challenge for archaeologists. In this work, we built a dataset named OBI-100, which contains 100 classes of oracle bone inscriptions collected from two OBI dictionaries. The dataset includes more than 128,000 character samples related to the natural environment, humans, animals, plants, etc. In addition, we propose improved models based on three typical deep convolutional network structures to recognize the OBI-100 dataset. By modifying the parameters, adjusting the network structures, and adopting optimization strategies, we demonstrate experimentally that these models perform fairly well in OBI recognition. For the 100-category OBI classification task, the optimal model achieves an accuracy of 99.5%, which shows competitive performance compared with other state-of-the-art approaches. We hope that this work can provide a valuable tool for character recognition of OBI.
- 9 January 2022
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 11 (1) , 45
Open AccessImprovement of Oracle Bone Inscription Recognition Accuracy: A Deep Learning Perspective
Deep learning techniques have been successfully applied in handwriting recognition. Oracle bone inscriptions (OBI) are the earliest hieroglyphs in China and valuable resources for studying the etymology of Chinese characters. OBI are of important historical and cultural value in China; thus, textual research surrounding the characters of OBI is a huge challenge for archaeologists. In this work, we built a dataset named OBI-100, which contains 100 classes of oracle bone inscriptions collected from two OBI dictionaries. The dataset includes more than 128,000 character samples related to the natural environment, humans, animals, plants, etc. In addition, we propose improved models based on three typical deep convolutional network structures to recognize the OBI-100 dataset. By modifying the parameters, adjusting the network structures, and adopting optimization strategies, we demonstrate experimentally that these models perform fairly well in OBI recognition. For the 100-category OBI classification task, the optimal model achieves an accuracy of 99.5%, which shows competitive performance compared with other state-of-the-art approaches. We hope that this work can provide a valuable tool for character recognition of OBI.