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Improved firefly algorithm-based optimized convolution neural network for scene character recognition

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Abstract

The most common and challenging issues in image recognition are scene character recognition from the street view image, and the scene character consists of both text and number. Recently, the researchers were introduced a lot of scene character recognition methods, but the performance of the methods often degraded due to complexity. So, we proposed the improved firefly algorithm for local trapping problem (IFLT) utilizing convolutional neural network (CNN) for the extraction of features from the scene character. The IFLT approach is the improved version of the firefly optimization algorithm to solve local trapping problems. During feature extraction, the hyperparameters on CNN are tuned with the help of the IFLT approach. The alignment and multilayer perceptron layers are used on CNN. Subsequently, the support vector machine approach is used to classify the relevant class of scene characters from the street view image. Experimentally, we use six scene character dataset SVHN, ISN, IIIT5K-words, SVT, ICDAR 2003, and ICDAR 2013 dataset. The performance of the proposed IFLT approach is evaluated with standard deviation, mean, average computational time, and most excellent minimum (MEmin) parameters. The experimental results demonstrate that the proposed IFLT-CNN is well suitable for scene character recognition.

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References

  1. Guo, Q., Wang, F., Lei, J., Tu, D., Li, G.: Convolutional feature learning and Hybrid CNN-HMM for scene character recognition. Neurocomputing 184, 78–90 (2016)

    Article  Google Scholar 

  2. Olga, Russakovsky, Deng, Jia, Hao, Su, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng: Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  3. Fraz, M., Sarfraz, M.S., Edirisinghe, E.A.: Exploiting colour information for better scene text detection and recognition. Int J Document Anal Recognit 18(2), 153–167 (2015)

    Article  Google Scholar 

  4. Huang, Y., Sun, Z., Jin, L., Luo, C.: EPAN: effective parts attention network for scene text recognition. Neurocomputing 376, 202–213 (2019)

    Article  Google Scholar 

  5. Luo, C., Jin, L., Sun, Z.: Moran: a multi-object rectified attention network for scene text recognition. Pattern Recognit 90, 109–118 (2019)

    Article  Google Scholar 

  6. Chen, X., Wang, T., Zhu, Y., Jin, L., Luo, C.: Adaptive embedding gate for attention-based scene text recognition. Neurocomputing 381, 261–271 (2019)

    Article  Google Scholar 

  7. Charles, R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3d classification and segmentation, (2016)

  8. Vinyals, O., Bengio, S., Kudlur, M.: Order matters: sequence to sequence for sets. Comput. Sci. (2015)

  9. Yang, X.-S.: Nature-inspired metaheuristic algorithms. Luniver press, London (2010)

    Google Scholar 

  10. Mishra, A., Alahari, K., Jawahar, C.V.: Scene text recognition using higher order language priors (2012)

  11. Lucas, S., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R.: ICDAR 2003 robust reading competitions. In: Seventh international conference on document analysis and recognition, pp 682–687 (2003)

  12. Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., Gomez i Bigorda, L., Mestre, S.R., Mas, J., Mota, D.F., Almazan, J.A., De Las Heras, L.P.: ICDAR 2013 robust reading competition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1484–1493 (2013)

  13. Wang, K., Babenko, B., Belongie, S.: End-to-end scene text recognition. In: 2011 International Conference on Computer Vision, pp 1457–1464 (2011)

  14. Baoguang, Shi, Yang, Mingkun, Wang, Xinggang, Lyu, Pengyuan, Yao, Cong, Bai, Xiang: Aster: an attentional scene text recognizer with flexible rectification. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2035–2048 (2018)

    Google Scholar 

  15. Xingyuan, Zhang, Huang, Yaping, Zou, Qi, Pei, Yanting, Zhang, Runsheng, Wang, Song: A hybrid convolutional neural network for sketch recognition. Pattern Recognit Lett 130, 73–82 (2019)

    Google Scholar 

  16. Liao, M., Zhang, J., Wan, Z., Xie, F., Liang, J., Lyu, P., Bai, X.: Scene text recognition from two-dimensional perspective. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 8714–8721, (2019)

  17. Gao, Y., Chen, Y., Wang, J., Tang, M., Lu, H.: Reading scene text with fully convolutional sequence modeling. Neurocomputing 339, 161–170 (2019)

    Article  Google Scholar 

  18. Zhang, X., Huang, Y., Zou, Q., Pei, Y., Zhang, R., Wang, S.: A hybrid convolutional neural network for sketch recognition. Pattern Recognit. Lett. 130, 73–82 (2020)

    Article  Google Scholar 

  19. Wang, Y., Huang, F., Zhang, Y., Feng, R., Zhang, T., Fan, W.: Deep cascaded cross-modal correlation learning for fine-grained sketch-based image retrieval. Pattern Recognit. 100, 107148 (2020)

    Article  Google Scholar 

  20. Bai, C., Chen, J., Ma, Q., Hao, P., Chen, S.: Cross-domain representation learning by domain-migration generative adversarial network for sketch based image retrieval. J. Visual Commun. Image Represent. 71, 102835 (2020)

    Article  Google Scholar 

  21. Han, J.H., Choi, D.J., Park, S.U., Hong, S.K.: Hyperparameter optimization using a genetic algorithm considering verification time in a convolutional neural network. J. Electric. Eng. Technol. 15(2), 721–726 (2020)

    Article  Google Scholar 

  22. Sundararaj, V.: An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int. J. Intell. Eng. Syst. 9(3), 117–126 (2016)

    Google Scholar 

  23. Vinu, S., Muthukumar, S., Kumar, R.S.: An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput. Secur. 77, 277–288 (2018)

    Article  Google Scholar 

  24. Sundararaj, V.: Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Personal Commun. 104(1), 173–197 (2019)

    Article  Google Scholar 

  25. Sundararaj, Vinu: Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction”. Int. J. Biomed. Eng. Technol. 31(4), 325–345 (2019)

    Article  Google Scholar 

  26. Thangakrishnan, M.S., Ramar, K.: Automated Hand-drawn sketches retrieval and recognition using regularized Particle Swarm Optimization based deep convolutional neural network. J. Ambient Intell. Human. Comput. 26, 1–13 (2020)

    Google Scholar 

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Correspondence to L. T. Akin Sherly.

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Akin Sherly, L.T., Jaya, T. Improved firefly algorithm-based optimized convolution neural network for scene character recognition. SIViP 15, 885–893 (2021). https://doi.org/10.1007/s11760-020-01810-4

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  • DOI: https://doi.org/10.1007/s11760-020-01810-4

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