Abstract
How to effectively identify these signals and data has become an urgent topic. The neural network model is a stochastic system composed of nonlinear neurons. Therefore, it has strong self adaptability and controllability. This paper proposes a method based on training samples. It classifies the original continuous text through the artificial neural network algorithm. This paper mainly uses experimental method and comparative method to analyze the accuracy, precision, recall rate, F value and its trend in training and the results under different models. The experimental results show that good results have been achieved on the IMDB comment dataset, and the accuracy rate is close to 89.4%.
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04 August 2023
A correction has been published.
References
Abdulqader, D.A., Hathal, M.S., Mahmmod, B.M., Abdulhussain, S.H., Al-Jumeily, D.: Plain, edge, and texture detection based on orthogonal moment. IEEE Access 10, 114455–114468 (2022)
Al-Dyani, W.Z., Ahmad, F.K., Kamaruddin, S.S.: Adaptive binary bat and markov clustering algorithms for optimal text feature selection in news events detection model. IEEE Access 10, 85655–85676 (2022
Ali, T., Siddiqui, M.F.H., Shahab, S., Roy, P.P.: GMIF: a gated multiscale input feature fusion scheme for scene text detection. IEEE Access 10, 93992–94006 (2022).
Al Besani, G., Alsulmi, M.: Exploring transformer-based learning for negation detection in biomedical texts. IEEE Access 10, 83813–83825 (2022)
Contreras, R.C., et al.: A new multi-filter framework for texture image representation improvement using set of pattern descriptors to fingerprint liveness detection. IEEE Access 10, 117681–117706 (2022)
Hwang, S., Lee, J., Kang, S.: Enabling product recognition and tracking based on text detection for mobile augmented reality. IEEE Access 10, 98769–98782 (2022)
Muhongo, T., Brazdil, P., Silva, F.: Detection of loanwords in angolan portuguese: a text mining approach. Inteligencia Artif. 25(69), 87–106 (2022)
Gupta, N., Jalal, A.S.: Traditional to transfer learning progression on scene text detection and recognition: a survey. Artif. Intell. Rev. 55(4), 3457–3502 (2022)
Company-Corcoles, J.P., Garcia-Fidalgo, E., Ortiz, A.: Appearance-based loop closure detection combining lines and learned points for low-textured environments. Auton. Robots 46(3), 451–467 (2022).
Chaudhary, M., Vashistha, S., Bansal, D.: Automated detection of anti-national textual response to terroristic events on online media. Cybern. Syst. 53(8), 702–715 (2022)
Daniya, T., Vigneshwari, S.: Deep neural network for disease detection in rice plant using the texture and deep features. Comput. J. 65(7), 1812–1825 (2022)
Singh, T., Kumari, M.: Daya sagar gupta: real-time event detection and classification in social text steam using embedding. Clust. Comput. 25(6), 3799–3817 (2022)
Mithila, T., Arunprakash, R., Ramachandran, A.: CNN and Fuzzy rules based text detection and recognition from natural scenes. Comput. Syst. Sci. Eng. 42(3), 1165–1179 (2022)
Malandrino, D., De Prisco, R., Ianulardo, M., Zaccagnino, R.: An adaptive meta-heuristic for music plagiarism detection based on text similarity and clustering. Data Min. Knowl. Discov. 36(4), 1301–1334 (2022)
Angel Deborah, S., Rajendram, S.M., Mirnalinee, T.T., Sivanaiah, R.: Contextual emotion detection on text using gaussian process and tree based classifiers. Intell. Data Anal. 26(1), 119–132 (2022)
Mansouri, S., Charhad, M., Zrigui, M.: A new approach for automatic arabic-text detection and localisation in video frames. Int. J. Adv. Intell. Paradigms 22(1/2), 72–83 (2022)
Khan, T., Mollah, A.F.: A two-stage text detection approach using gradient point adjacency and deep network. Int. J. Comput. Sci. Eng. 25(2), 152–165 (2022)
Boillet, M., Kermorvant, C., Paquet, T.: Robust text line detection in historical documents: learning and evaluation methods. Int. J. Document Anal. Recognit. 25(2), 95–114 (2022)
Naosekpam, V., Sahu, N.: Text detection, recognition, and script identification in natural scene images: a review. Int. J. Multim. Inf. Retr. 11(3), 291–314 (2022)
Zhong, D., Shivakumara, P., Nandanwar, L., Pal, U., Blumenstein, M., Lu, Y.: Local resultant gradient vector difference and inpainting for 3D text detection in the wild. Int. J. Pattern Recognit. Artif. Intell. 36(8), 2253005:1–2253005:25 (2022)
Acknowledgements
This work was supported by: Key Research Project of Guangdong Baiyun College, No. 2022BYKYZ02; Key Research Platform of Guangdong Province, No. 2022GCZX009; Special project in key fields of colleges and universities in Guangdong province, No. 2020ZDZX3009.
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Qi, Y., Tang, H., Huang, L. (2023). Double Attention Mechanism Text Detection and Recognition Based on Neural Network Algorithm. In: Hung, J.C., Chang, JW., Pei, Y. (eds) Innovative Computing Vol 1 - Emerging Topics in Artificial Intelligence. IC 2023. Lecture Notes in Electrical Engineering, vol 1044. Springer, Singapore. https://doi.org/10.1007/978-981-99-2092-1_64
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DOI: https://doi.org/10.1007/978-981-99-2092-1_64
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