Advanced Dense Text Detection in Graded Examinations Leveraging Chinese Character Components
Source abstract
Abstract
The dense text detection and segmentation of Chinese characters has always been a research hotspot due to the complex background and diverse scenarios. In the field of education, the detection of handwritten Chinese characters is affected by background noise, texture interference, etc. Especially in low-quality handwritten text, character overlap or occlusion makes the character boundaries blurred, which increases the difficulty of detection and segmentation; In this paper, an improved EAST network CEE (Components-ECA-EAST Network), which fuses the attention mechanism with the feature pyramid structure, is proposed based on the analysis of the structure of Chinese character mini-components. The ECA (Efficient Channel Attention) attention mechanism is incorporated during the feature extraction phase; in the feature fusion stage, the convolutional features are extracted from the self-constructed mini-component dataset and then fused with the feature pyramid in a cascade manner, and finally, Dice Loss is used as the regression task loss function. The above improvements comprehensively improve the performance of the network in detecting and segmenting the mini-components and subtle strokes of handwritten Chinese characters; The CEE model was tested on the self-constructed dataset with an accuracy of 84.6% and a mini-component mAP of 77.6%, which is an improvement of 7.4% and 8.4%, respectively, compared to the original model; The constructed dataset and improved model are well suited for applications such as writing grade examinations, and represent an important exploration of the development of educational intelligence.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Handwritten Text Recognition Techniques · Natural Language Processing Techniques · Text and Document Classification Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
Funding
University-Industry Co-operation Project, Intelligent Sensing and Real-time Monitoring System for Urban Spatial Light Environment, Shanghai, China
References (31)
Show source-provided references
1. Wang, K., Babenko, B., and Belongie, S. (2011, January 6–13). End-to-end scene text recognition. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.
2. Tian, Z., Huang, W., He, T., He, P., and Qiao, Y. (2016, January 11–14). Detecting text in natural image with connectionist text proposal network. Proceedings of the Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part VIII 14.10.1007/978-3-319-46484-8_4 ↗
3. Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., and Liang, J. (2017, January 21–26). East: An efficient and accurate scene text detector. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.283 ↗
4. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18.
5. Shi, B., Bai, X., and Belongie, S. (2017, January 21–26). Detecting oriented text in natural images by linking segments. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.371 ↗
6. Deng, D., Liu, H., Li, X., and Cai, D. (2018, January 2–7). Pixellink: Detecting scene text via instance segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.10.1609/aaai.v32i1.12269 ↗
7. Long, S., Ruan, J., Zhang, W., He, X., Wu, W., and Yao, C. (2018, January 8–14). Textsnake: A flexible representation for detecting text of arbitrary shapes. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01216-8_2 ↗
8. Wang, H., Lu, P., Zhang, H., Yang, M., Bai, X., Xu, Y., He, M., Wang, Y., and Liu, W. (2020, January 7–12). All you need is boundary: Toward arbitrary-shaped text spotting. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6896 ↗
9. Text Detection of Transformer Based on Deep Learning Algorithm
10. TextFuse: Fusing Deep Scene Text Detection Models for Enhanced Performance10.1007/s11042-023-16389-4 ↗
11. Liu, M., Li, B., and Zhang, W. (2022). Research on Small Acceptance Domain Text Detection Algorithm Based on Attention Mechanism and Hybrid Feature Pyramid. Electronics, 11.10.3390/electronics11213559 ↗
12. Yuan, L., Zeng, C., and Pan, P. (2024). Research of Chinese Entity Recognition Model Based on Multi-Feature Semantic Enhancement. Electronics, 13.10.3390/electronics13244895 ↗
13. Wawer, A., Mykowiecka, A., and Żuk, B. (2024). Detecting Aggression in Language: From Diverse Data to Robust Classifiers. Electronics, 13.10.3390/electronics13244857 ↗
14. Xue, Y., Wang, Q., Hu, Y., Qian, Y., Cheng, L., and Wang, H. (2024). FL-YOLOv8: Lightweight Object Detector Based on Feature Fusion. Electronics, 13.10.3390/electronics13234653 ↗
15. Ren, Q.-D.-E.-J., Wang, L., Ma, Z., and Barintag, S. (2024). Offline Mongolian Handwriting Recognition Based on Data Augmentation and Improved ECA-Net. Electronics, 13.10.3390/electronics13050835 ↗
16. Qian, K., Ding, X., Jiang, X., Ji, Y., and Dong, L. (2024). CFF-Net: Cross-Hierarchy Feature Fusion Network Based on Composite Dual-Channel Encoder for Surface Defect Segmentation. Electronics, 13.10.3390/electronics13234714 ↗
17. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
18. Park, J. (2018). Bam: Bottleneck attention module. arXiv.
19. Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8–14). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
20. Cao, Y., Xu, J., Lin, S., Wei, F., and Hu, H. (2019, January 27–28). Gcnet: Non-local networks meet squeeze-excitation networks and beyond. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea.10.1109/ICCVW.2019.00246 ↗
21. Hu, J., Shen, L., and Sun, G. (2018, January 18–23). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
22. Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 9–15). Self-attention generative adversarial networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA.
23. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13–19). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.01155 ↗
24. Liu, R., Shi, Y., Tang, X., and Liu, X. (2025). Combining Multi-Scale Fusion and Attentional Mechanisms for Assessing Writing Accuracy. Appl. Sci., 15.10.3390/app15031204 ↗
25. Model-agnostic feature importance and effects with dependent features: A conditional subgroup approach10.1007/s10618-022-00901-9 ↗
26. A tutorial on the cross-entropy method10.1007/s10479-005-5724-z ↗
27. A deep learning approach for robust, multi-oriented, and curved text detection10.1007/s12559-022-10072-w ↗
28. Zhao, R., Qian, B., Zhang, X., Li, Y., Wei, R., Liu, Y., and Pan, Y. (2020, January 17–20). Rethinking dice loss for medical image segmentation. Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, Italy.10.1109/ICDM50108.2020.00094 ↗
29. Deep learning-based text detection and recognition on architectural floor plans10.1016/j.autcon.2023.105156 ↗
30. Peyrard, C., Baccouche, M., Mamalet, F., and Garcia, C. (2015, January 23–26). ICDAR2015 Competition on Text Image Super-Resolution. Proceedings of the International Conference on Document Analysis & Recognition, Tunis, Tunisia.10.1109/ICDAR.2015.7333951 ↗
31. A path towards autonomous machine intelligence version 0.9.2, 2022-06-27