EFFICIENCY COMPARISON OF NETWORKS IN HANDWRITTEN LATIN CHARACTERS RECOGNITION WITH DIACRITICS
Edyta ŁUKASIK
e.lukasik@pollub.plLublin University of Technology (Poland)
https://orcid.org/0000-0003-3644-9769
Wiktor FLIS
Lublin University of Technology (Poland)
https://orcid.org/0009-0002-6804-6630
Abstract
The aim of the article is to analyze and compare the performance and accuracy of architectures with a different number of parameters on the example of a set of handwritten Latin characters from the Polish Handwritten Characters Database (PHCD). It is a database of handwriting scans containing letters of the Latin alphabet as well as diacritics characteristic of the Polish language. Each class in the PHCD dataset contains 6,000 scans for each character. The research was carried out on six proposed architectures and compared with the architecture from the literature. Each of the models was trained for 50 epochs, and then the accuracy of prediction was measured on a separate test set. The experiment thus constructed was repeated 20 times for each model. Accuracy, number of parameters and number of floating-point operations performed by the network were compared. The research was conducted on subsets such as uppercase letters, lowercase letters, lowercase letters with diacritics, and a subset of all available characters. The relationship between the number of parameters and the accuracy of the model was indicated. Among the examined architectures, those that significantly improved the prediction accuracy at the expense of a larger network size were selected, and a network with a similar prediction accuracy as the base one, but with twice as many model parameters was selected.
Keywords:
convolutional neural network, model efficiency, handwritten text recognitionReferences
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., … Zheng, X. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. ArXiv, abs/1603.04467. https://doi.org/10.48550/ARXIV.1603.04467
Google Scholar
Belkin, M., Hsu, D., Ma, S., & Mandal, S. (2019). Reconciling modern machine-learning practice and the classical bias-variance trade-off. Proceedings of the National Academy of Sciences, 116(32), 15849-15854. https://doi.org/10.1073/pnas.1903070116
DOI: https://doi.org/10.1073/pnas.1903070116
Google Scholar
Blalock, D., Ortiz, J. J. G., Frankle, J., & Guttag, J. (2020). What is the state of neural network pruning?. ArXiv, abs/2003.03033. https://doi.org/10.48550/arXiv.2003.03033
Google Scholar
Bouthillier, X., Delaunay, P., Bronzi, M., Trofimov, A., Nichyporuk, B., Szeto, J., Sepah, N., Raff, E., Madan, K., Voleti, V., Kahou, S. E., Michalski, V., Serdyuk, D., Arbel, T., Pal, C., Varoquaux, G., & Vincent, P. (2021). Accounting for variance in machine learning benchmarks. ArXiv, abs/2103.03098. https://doi.org/10.48550/ARXIV.2103.03098
Google Scholar
Choi, Y., El-Khamy, M., & Lee, J. (2016). Towards the limit of network quantization. ArXiv, abs/1612.01543. https://doi.org/10.48550/arXiv.1612.01543
Google Scholar
Cohen, G., Afshar, S., Tapson, J., & Van Schaik, A. (2017). EMNIST: Extending MNIST to handwritten letters. 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2921-2926). IEEE. https://doi.org/10.1109/IJCNN.2017.7966217
DOI: https://doi.org/10.1109/IJCNN.2017.7966217
Google Scholar
Gajoui, K. E., Allah, F. A., & Oumsis, M. (2015). Diacritical language OCR based on neural network: Case of amazigh language. Procedia Computer Science, 73, 298‒305. https://doi.org/10.1016/j.procs.2015.12.035
DOI: https://doi.org/10.1016/j.procs.2015.12.035
Google Scholar
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. ArXiv, abs/1512.07108. https://doi.org/10.48550/arXiv.1512.07108
DOI: https://doi.org/10.1016/j.patcog.2017.10.013
Google Scholar
Hadidi, R., Cao, J., Xie, Y., Asgari, B., Krishna, T., & Kim, H. (2019). Characterizing the deployment of deep neural networks on commercial edge devices. 2019 IEEE International Symposium on Workload Characterization (IISWC) (pp. 35-48). IEEE. https://doi.org/10.1109/IISWC47752.2019.9041955
DOI: https://doi.org/10.1109/IISWC47752.2019.9041955
Google Scholar
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778). IEEE. https://doi.org/10.1109/CVPR.2016.90
DOI: https://doi.org/10.1109/CVPR.2016.90
Google Scholar
Idziak, J., Šeļa, A., Woźniak, M., Leśniak, A., Byszuk, J., & Eder, M. (2021). Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets. In International Conference on Computational Science 2021 (pp. 137–150). Springer. https://doi.org/10.1007/978-3-030-77961-0_13
DOI: https://doi.org/10.1007/978-3-030-77961-0_13
Google Scholar
Islam, N., Islam, Z., & Noor, N. (2017). A Survey on optical character recognition system. ArXiv, abs/1710.05703. https://doi.org/10.48550/arXiv.1710.05703
Google Scholar
Lukasik, E., Charytanowicz, M., Milosz, M., Tokovarov, M., Kaczorowska, M., Czerwinski, D., & Zientarski, T. (2021). Recognition of handwritten Latin characters with diacritics using CNN. Bulletin of the Polish Academy of Sciences: Technical Sciences, 69(1), e136210. https://doi.org/10.24425/bpasts.2020.136210
DOI: https://doi.org/10.24425/bpasts.2020.136210
Google Scholar
Lutf, M., You, X., Cheung, Y., & Chen, C. (2014). Arabic font recognition based on diacritics features. Pattern Recognition, 47(2), 672–684. https://doi.org/10.1016/j.patcog.2013.07.015
DOI: https://doi.org/10.1016/j.patcog.2013.07.015
Google Scholar
Łukasik, E.,& Zientarski, T. (2018). Comparative analysis of selected programs for optical text recognition. Journal of Computer Sciences Institute, 7, 191-194. https://doi.org/10.35784/jcsi.676
DOI: https://doi.org/10.35784/jcsi.676
Google Scholar
Sharma, R., Kaushik, B., & Gondhi, N. (2020). Character recognition using machine learning and deep learning - a survey. 2020 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 341-345). IEEE. http://doi.org/10.1109/ESCI48226.2020.9167649
DOI: https://doi.org/10.1109/ESCI48226.2020.9167649
Google Scholar
Tokovarov, M., Kaczorowska, M., & Milosz, M. (2020). Development of extensive polish handwritten characters database for text recognition research. Advances in Science and Technology Research Journal, 14(3), 30-38. https://doi.org/10.12913/22998624/122567
DOI: https://doi.org/10.12913/22998624/122567
Google Scholar
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. ArXiv, abs/1706.03762. https://doi.org/10.48550/arXiv.1706.03762
Google Scholar
Wang, H., Qin, C., Bai, Y., Zhang, Y., & Fu, Y. (2022). Recent advances on neural network pruning at initialization. ArXiv, abs/2103.06460. https://doi.org/10.48550/arXiv.2103.06460
DOI: https://doi.org/10.24963/ijcai.2022/786
Google Scholar
Authors
Edyta ŁUKASIKe.lukasik@pollub.pl
Lublin University of Technology Poland
https://orcid.org/0000-0003-3644-9769
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