融合空间特征的债券图表数据文本检测方法研究
Text Detection Method for Bond Chart Data Fusing Spatial Features
摘要: 随着国家明确了金融业发展和改革的重点方向，我国金融数据信息化有了显著的发展和进步。基于债券图表数据的特定情况，人工处理债券图表数据存在效率低、成本高、安全性低等问题，用人工智能的方法来检测债券图表数据逐渐成为了当下的热门研究方向。由于债券图表数据在长时间存放、人为损坏等主客观因素下，会存在模糊、被污染等特点。对此本文使用了Swin-Transformer作为主干网络，它的特征提取能力较CNN (卷积神经网络)更为强大。并对模糊、污染的区域设计了方向感知模块，使其对文本区域的识别正确率更高。实验结果表明，该网络比其它文本检测算法在准确率、召回率、F1值上都有明显提升。
Abstract: With the clear direction of financial development and reform, China’s financial data information technology has made significant progress and development. Based on the specific situation of bond chart data, manual processing of bond chart data is inefficient, high cost, low security, and so on. Using artificial intelligence to detect bond chart data has gradually become a hot research direction. Because the bond chart data is stored for a long time and damaged artificially, it will be fuzzy and polluted. In this paper, Swin-Transformer is used as the backbone network, and its feature extraction ability is stronger than that of CNN (convolution network). Direction perception module is de-signed for blurred and contaminated areas, which makes the recognition of text areas more accurate. The experimental results show that the network improves the accuracy, recall and F1 value significantly compared with other text detection algorithms.
文章引用：李桂钢, 胡金蓉, 帅梓涵, 郎子鑫, 罗月梅. 融合空间特征的债券图表数据文本检测方法研究[J]. 数据挖掘, 2023, 13(2): 143-153. https://doi.org/10.12677/HJDM.2023.132014

参考文献

[1] 董小君, 宋玉茹. 加快推进我国金融数据治理现代化建设研究[J]. 行政与法, 2022(8): 11-21.
[2] Liu, Z., Lin, Y., Cao, Y., et al. (2021) Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. IEEE International Conference on Computer Vision (ICCV), Montreal, 10-17 October 2021, 10012-10022. [Google Scholar] [CrossRef] 
[3] Elman, J.L. (1990) Finding Structure in Time. Cognitive Sci-ence, 14, 179-211. [Google Scholar] [CrossRef] 
[4] Vaswani, A., Shazeer, N., Parmar, N., et al. (2017) Attention Is All You Need. 31st Conference on Neural Information Processing Systems, Long Beach, December 2017, 6000-6010.
[5] Hochreiter, S. and Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9, 1735-1780. [Google Scholar] [CrossRef] [PubMed]
[6] Zhong, Y., Karu, K. and Jain, A.K. (1995) Locating Text in Com-plex Color Images. Pattern Recognition, 28, 1523-1535. [Google Scholar] [CrossRef] 
[7] Kim, K.I., Jung, K. and Kim, J.H. (2003) Texture-Based Ap-proach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 1631-1639. [Google Scholar] [CrossRef] 
[8] Minetto, R., Thome, N., Cord, M., Leite, N.J. and Stolfi, J. (2013) T-HOG: An Effective Gradient-Based Descriptor for Single Line Text Regions. Pattern Recognition, 46, 1078-1090. [Google Scholar] [CrossRef] 
[9] Kim, Y. (2014) Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, October 2014, 1746-1751. [Google Scholar] [CrossRef] 
[10] Wang, T., Wu, D.J., Coates, A. and Ng, A.Y. (2012) End-to-End Text Recognition with Convolutional Neural Networks. Proceedings of the 21st International Conference on Pattern Recogni-tion, Tsukuba, 11-15 November 2012, 3304-3308.
[11] Jaderberg, M., Vedaldi, A. and Zisserman, A. (2014) Deep Features for Text Spotting. Proceedings of the 13th European Conference on Computer Vision, Zurich, 6-12 September 2014, 512-528. [Google Scholar] [CrossRef] 
[12] Epshtein, B., Ofek, E. and Wexler, Y. (2010) Detecting Text in Natural Scenes with Stroke width Transform. Proceedings of 2010 IEEE Computer Society Conference on Computer Vi-sion and Pattern Recognition, San Francisco, 13-18 June 2010, 2963-2970. [Google Scholar] [CrossRef] 
[13] Matas, J., Chum, O., Urban, M. and Pajdla, T. (2004) Robust Wide-Baseline Stereo from Maximally Stable Extremal Regions. Image and Vision Computing, 22, 761-767. [Google Scholar] [CrossRef] 
[14] Tian, Z., Huang, W.L., He, T., He, P. and Qiao, Y. (2016) De-tecting Text in Natural Image with Connectionist Text Proposal Network. Proceedings of the 14th European Conference on Computer Vision, Amsterdam, 11-14 October 2016, 56-72. [Google Scholar] [CrossRef] 
[15] Zhou, X.Y., Yao, C., Wen, H., et al. (2017) EAST: An Effi-cient and Accurate Scene Text Detector. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recog-nition, Honolulu, 21-26 July 2017, 5551-5560. [Google Scholar] [CrossRef] 
[16] Ma, J.Q., Shao, W.Y., Ye, H., et al. (2018) Arbitrary-Oriented Scene Text Detection via Rotation Proposals. IEEE Transactions on Multimedia, 20, 3111-3122. [Google Scholar] [CrossRef] 
[17] Zhang, Z., Zhang, C.Q., Shen, W., et al. (2016) Multi-Oriented Text Detection with Fully Convolutional Networks. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 4159-4167. [Google Scholar] [CrossRef] 
[18] Deng, D., Liu, H.F., Li, X.L. and Cai, D. (2018) Pixellink: Detecting Scene Text via Instance Segmentation. Proceedings of the 32nd AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th Innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, 2-7 February 2018, 6773-6780. [Google Scholar] [CrossRef] 
[19] Liao, M.H., Wan, Z.Y., Yao, C., Chen, K. and Bai, X. (2020) Real-Time Scene Text Detection with Differentiable Binarization. Proceedings of the 34th AAAI Conference on Artificial Intelligence, AAAI 2020, the 32nd Innovative Applications of Artificial Intelligence Conference, IAAI 2020, the 10th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, 7-12 February 2020, 11474-11481. [Google Scholar] [CrossRef] 
[20] Lint, Y., Dollar, P., Girshick, R., et al. (2017) Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 21-26 July 2017, 2117-2125. [Google Scholar] [CrossRef] 

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