Filtering Approaches and Mish Activation Function Applied on Handwritten Chinese Character Recognition
Abstract Handwritten Chinese Characters (HCC) have recently received much attention as a global means of exchanging information and knowledge. The start of the information age has increased the number of paper documents that must be electronically saved and shared. The recognition accuracy of online handwritten Chinese characters has reached its limit as online characters are more straightforward than offline characters. Furthermore, online character recognition enables stronger involvement and flexibility than offline characters. Deep learning techniques, such as convolutional neural networks (CNN), have superseded conventional Handwritten Chinese Character Recognition (HCCR) solutions, as proven in image identification. Nonetheless, because of the large number of comparable characters and styles, there is still an opportunity to improve the present recognition accuracy by adopting different activation functions, including Mish, Sigmoid, Tanh, and ReLU. The main goal of this study is to apply a filter and activation function that has a better impact on the recognition system to improve the performance of the recognition CNN model. In this study, we implemented different filter techniques and activation functions in CNN to offline Chinese characters to understand the effects of the model's recognition outcome. Two CNN layers are proposed given that they achieve comparative performances using fewer-layer CNN. The results demonstrate that the Weiner filter has better recognition performance than the median and average filters. Furthermore, the Mish activation function performs better than the Sigmoid, Tanh, and ReLU functions.
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Aljojo, N. (2022). Predicting Financial Risk Associatedwitho Bitcoin Investment by Deep Learning. ADCAIJ: Advances in Distributed Computing and Artificial Intel-ligence Journal, 11(1), 5-18. https://doi.org/10.14201/adcaij.27269
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Ciresan, D. C., Meier, U., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2011a). Flexible, high performance convolutional neural networks for image classification. Twenty-Second International Joint Conference on Artificial Intelligence.
Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2011b). Convolutional neural network committees for handwritten character classification. 2011 International Conference on Document Analysis and Recognition. IEEE. https://doi.org/10.1109/ICDAR.2011.229
Dai R., Liu C. and Xiao B., 2007. Chinese character recognition: history, status and pro-spects. Frontiers of Computer Science in China, 1(2), 126-136. https://doi.org/10.1007/s11704-007-0012-5
Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). IEEE.
Dass, R., & Saini, J. (2022). Assessment of de-noising filters for brain MRI T1-weighted contrast-enhanced images. In Emergent Converging Technologies and Biomedical Systems (pp. 607-613). Springer. https://doi.org/10.1007/978-981-16-8774-7_50
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Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., & Shet, V. (2013). Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv preprint arXiv:1312.6082.
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Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J. & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.
Kavitha, B. R., & Srimathi, C. (2019). Benchmarking on offline handwritten Tamil character recognition using convolutional neural networks. Journal of King Saud University - Computer and Information Sciences.
Kazubek, M. (2003). Wavelet domain image denoising by thresholding and Wiener filter-ing. IEEE Signal Processing Letters 10(11), 324-326. https://doi.org/10.1109/LSP.2003.818225
Kumar, A. A., Lal, N., & Kumar, R. N. (2021). A comparative study of various filtering techniques. 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE. https://doi.org/10.1109/ICOEI51242.2021.9453068
Li, Y., & Li, Y. (2021). Design and implementation of handwritten Chinese character recognition method based on CNN and TensorFlow. 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE. https://doi.org/10.1109/ICAICA52286.2021.9498146
Li, Z., Teng, N., Jin, M., & Lu, H. (2018). Building efficient CNN architecture for offline handwritten Chinese character recognition. International Journal on Document Analysis and Recognition (IJDAR), 21(4), 233-240. https://doi.org/10.1007/s10032-018-0311-4
Li, Z., Xiao, Y., Wu, Q., Jin, M., & Lu, H. (2020). Deep template matching for offline handwritten Chinese character recognition. The Journal of Engineering, 2020(4), 120-124. https://doi.org/10.1049/joe.2019.0895
Liu, B., Xu, X., & Zhang, Y. (2020). Offline handwritten Chinese text recognition with convolutional neural networks. arXiv preprint arXiv:2006.15619.
Liu, C. L., Yin, F., Wang, D. H., & Wang, Q. F. (2011). CASIA online and offline Chinese handwriting databases. In 2011 International Conference on Document Analysis and Recognition (pp. 37-41). IEEE. https://doi.org/10.1109/ICDAR.2011.17
Liu, C. L. (2006). High accuracy handwritten Chinese character recognition using quadratic classifiers with discriminative feature extraction. 18th International Conference on Pattern Recognition (ICPR'06). IEEE.
Liu, C. L., Jaeger, S., & Nakagawa, M. (2004). Online recognition of Chinese characters: The state-of-the-art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2), 198-213. https://doi.org/10.1109/TPAMI.2004.1262182
Liu, H., & Ding, X. (2005). Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes. Eighth International Conference on Document Analysis and Recognition (ICDAR'05). IEEE.
Liu, X., & Di, X. (2021). TanhExp: A smooth activation function with high convergence speed for lightweight neural networks. IET Computer Vision 15(2), 136-150. https://doi.org/10.1049/cvi2.12020
Maniatopoulos, A., & Mitianoudis, N. (2021). Learnable Leaky ReLU (LeLeLU): An alternative accuracy-optimized activation function. Information, 12(12), 513. https://doi.org/10.3390/info12120513
Melnyk, P., You, Z., & Li, K. (2020). A high-performance CNN method for offline handwritten Chinese character recognition and visualization. Soft Computing, 24(11), 7977-7987. https://doi.org/10.1007/s00500-019-04083-3
Min, F., Zhu, S., & Wang, Y. (2020). Offline handwritten Chinese character recognition based on improved GoogLeNet. In Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition. https://doi.org/10.1145/3430199.3430202
Misra, D. (2019). Mish: A self regularized non-monotonic activation function. arXiv pre-print arXiv:1908.08681.
Ng, P. E., & Ma, K. K. (2006). A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Transactions on Image Processing, 15(6), 1506-1516. https://doi.org/10.1109/TIP.2005.871129
Patidar, P., Gupta, M., Srivastava, S., & Nagawat, A. K. (2010). Image denoising by various filters for different noise. International Journal of Computer Applications, 9(4), 45-50. https://doi.org/10.5120/1370-1846
Sakarkar, G., Kolekar, M. K. H., Paithankar, K., Patil, G., Dutta, P., Chaturvedi, R., & Kumar, S. (2021). Advance approach for detection of DNS tunneling attack from network packets using deep learning algorithms. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(3), 241-266.
Sakib, S., Ahmed, N., Kabir, A. J., & Ahmed, H. (2019). An overview of convolutional neural network: Its architecture and applications. https://doi.org/10.20944/preprints201811.0546.v4
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, C. L. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2018.00474
Shetti, P. P., & Patil, A. (2017). Performance comparison of mean, median, and Wiener filter in MRI image denoising. International Journal for Research Trends and Innovation, 2, 371-375.
Shi, M., Fujisawa, Y., Wakabayashi, T., & Kimura, F. (2002). Handwritten numeral recognition using gradient and curvature of gray scale image. Pattern Recognition, 35(10), 2051-2059. https://doi.org/10.1016/S0031-3203(01)00203-5
Srinivas, R. & Panda, S. (2013). Performance analysis of various filters for image noise removal in different noise environment. International journal of advanced computer research, 3(4), 47.
Sriporn, K., Tsai, C. F., Tsai, C. E., & Wang, P. (2020). Analyzing malaria disease using effective deep learning approach. Diagnostics, 10(10), 744. https://doi.org/10.3390/diagnostics10100744
Sun, Y. (2021). The role of activation function in image classification. In 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE. https://doi.org/10.1109/CISCE52179.2021.9445868
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2016.308
Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2014.220
Wang, Y., Yang, Y., Ding, W., & Li, S. (2021). A residual-attention offline handwritten Chinese text recognition based on fully convolutional neural networks. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3115606
Xiao, X., Jin, L., Yang, Y., Yang, W., Sun, J., & Chang, T. (2017). Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition. Pattern Recognition, 72, 72-81. https://doi.org/10.1016/j.patcog.2017.06.032
Xu, X., Yang, C., Wang, L., Zhong, J., Bao, W., & Guo, J. (2022). A sophisticated offline network developed for recognizing handwritten Chinese character efficiently. Computers and Electrical Engineering, 100, 107857. https://doi.org/10.1016/j.compeleceng.2022.107857
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Zhang, X. Y., Bengio, Y., & Liu, C. L. (2017). Online and offline handwritten Chinese character recognition: A comprehensive study and new benchmark. Pattern Recognition, 61, 348-360. https://doi.org/10.1016/j.patcog.2016.08.005
Zheng, J., Ding, X., & Wu, Y. (1997). Recognizing online handwritten Chinese characters via farg matching. In Proceedings of the Fourth International Conference on Document Analysis and Recognition. IEEE.
Zhong, Z., Jin, L., & Xie, Z. (2015). High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps. In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE. https://doi.org/10.1109/ICDAR.2015.7333881
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Aljojo, N. (2022). Predicting Financial Risk Associatedwitho Bitcoin Investment by Deep Learning. ADCAIJ: Advances in Distributed Computing and Artificial Intel-ligence Journal, 11(1), 5-18. https://doi.org/10.14201/adcaij.27269
Alom, M. Z., Sidike, P., Taha. T. M, and Asari, V. K. (2017). Handwritten bangla digit recognition using deep learning. arXiv preprint arXiv:1705.02680.
Ameri, R., Alameer, A., Ferdowsi, S., Abolghasemi, V., & Nazarpour, K. (2021). Classification of handwritten Chinese numbers with convolutional neural networks. 2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE. https://doi.org/10.1109/IPRIA53572.2021.9483557
Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., & Chen, G. (2016). Deep Speech 2: End-to-end speech recognition in English and Mandarin. International Conference on Machine Learning, PMLR.
Arya, M. C. and Semwal A., 2017. Comparison on Average, Median and Wiener Filter using Lung Images. IMAGE 1(37.42), 3.45.
Bi, N., Chen, J., & Tan, J. (2019). The handwritten Chinese character recognition uses convolutional neural networks with the GoogLeNet. International Journal of Pattern Recognition and Artificial Intelligence, 33(11), 1940016. https://doi.org/10.1142/S0218001419400160
Chen, L., Wang, S., Fan, W., Sun, J., & Naoi, S. (2015). Beyond human recognition: A CNN-based framework for handwritten character recognition. 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE. https://doi.org/10.1109/ACPR.2015.7486592
Church, J., Chen, C. Y., & Rice, S. V. (2008). A spatial median filter for noise removal in digital images. In IEEE SoutheastCon 2008 (pp. 618-623). IEEE. https://doi.org/10.1109/SECON.2008.4494367
Ciregan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column deep neural networks for image classification. 2012 IEEE conference on computer vision and pattern recog-nition. IEEE. https://doi.org/10.1109/CVPR.2012.6248110
Ciresan, D. C., Meier, U., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2011a). Flexible, high performance convolutional neural networks for image classification. Twenty-Second International Joint Conference on Artificial Intelligence.
Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2011b). Convolutional neural network committees for handwritten character classification. 2011 International Conference on Document Analysis and Recognition. IEEE. https://doi.org/10.1109/ICDAR.2011.229
Dai R., Liu C. and Xiao B., 2007. Chinese character recognition: history, status and pro-spects. Frontiers of Computer Science in China, 1(2), 126-136. https://doi.org/10.1007/s11704-007-0012-5
Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). IEEE.
Dass, R., & Saini, J. (2022). Assessment of de-noising filters for brain MRI T1-weighted contrast-enhanced images. In Emergent Converging Technologies and Biomedical Systems (pp. 607-613). Springer. https://doi.org/10.1007/978-981-16-8774-7_50
Daugman, J. G. (1988). Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. IEEE Transactions on acoustics, speech, and sig-nal processing 36(7), 1169-1179. https://doi.org/10.1109/29.1644
Gan, J., Wang, W., & Lu, K. (2020). Compressing the CNN architecture for in-air handwritten Chinese character recognition. Pattern Recognition Letters, 129, 190-197. https://doi.org/10.1016/j.patrec.2019.11.028
Ge, Y., Huo, Q., & Feng, Z. D. (2002). Offline recognition of handwritten Chinese characters using Gabor features, CDHMM modeling, and MCE training. 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE. https://doi.org/10.1109/ICASSP.2002.5743976
Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., & Shet, V. (2013). Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv preprint arXiv:1312.6082.
Guo, H., Ai, L., & Chen, S. (2020). Application of convolutional neural network in handwritten Chinese character recognition. In 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (Vol. 1, pp. 1278-1281). IEEE. https://doi.org/10.1109/ICIBA50161.2020.9277290
Hao, W., Yizhou, W., Yaqin, L., & Zhili, S. (2020). The role of activation function in CNN. 2020 2nd International Conference on Information Technology and Computer Application (ITCA). IEEE. https://doi.org/10.1109/ITCA52113.2020.00096
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J. & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.
Kavitha, B. R., & Srimathi, C. (2019). Benchmarking on offline handwritten Tamil character recognition using convolutional neural networks. Journal of King Saud University - Computer and Information Sciences.
Kazubek, M. (2003). Wavelet domain image denoising by thresholding and Wiener filter-ing. IEEE Signal Processing Letters 10(11), 324-326. https://doi.org/10.1109/LSP.2003.818225
Kumar, A. A., Lal, N., & Kumar, R. N. (2021). A comparative study of various filtering techniques. 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE. https://doi.org/10.1109/ICOEI51242.2021.9453068
Li, Y., & Li, Y. (2021). Design and implementation of handwritten Chinese character recognition method based on CNN and TensorFlow. 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE. https://doi.org/10.1109/ICAICA52286.2021.9498146
Li, Z., Teng, N., Jin, M., & Lu, H. (2018). Building efficient CNN architecture for offline handwritten Chinese character recognition. International Journal on Document Analysis and Recognition (IJDAR), 21(4), 233-240. https://doi.org/10.1007/s10032-018-0311-4
Li, Z., Xiao, Y., Wu, Q., Jin, M., & Lu, H. (2020). Deep template matching for offline handwritten Chinese character recognition. The Journal of Engineering, 2020(4), 120-124. https://doi.org/10.1049/joe.2019.0895
Liu, B., Xu, X., & Zhang, Y. (2020). Offline handwritten Chinese text recognition with convolutional neural networks. arXiv preprint arXiv:2006.15619.
Liu, C. L., Yin, F., Wang, D. H., & Wang, Q. F. (2011). CASIA online and offline Chinese handwriting databases. In 2011 International Conference on Document Analysis and Recognition (pp. 37-41). IEEE. https://doi.org/10.1109/ICDAR.2011.17
Liu, C. L. (2006). High accuracy handwritten Chinese character recognition using quadratic classifiers with discriminative feature extraction. 18th International Conference on Pattern Recognition (ICPR'06). IEEE.
Liu, C. L., Jaeger, S., & Nakagawa, M. (2004). Online recognition of Chinese characters: The state-of-the-art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2), 198-213. https://doi.org/10.1109/TPAMI.2004.1262182
Liu, H., & Ding, X. (2005). Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes. Eighth International Conference on Document Analysis and Recognition (ICDAR'05). IEEE.
Liu, X., & Di, X. (2021). TanhExp: A smooth activation function with high convergence speed for lightweight neural networks. IET Computer Vision 15(2), 136-150. https://doi.org/10.1049/cvi2.12020
Maniatopoulos, A., & Mitianoudis, N. (2021). Learnable Leaky ReLU (LeLeLU): An alternative accuracy-optimized activation function. Information, 12(12), 513. https://doi.org/10.3390/info12120513
Melnyk, P., You, Z., & Li, K. (2020). A high-performance CNN method for offline handwritten Chinese character recognition and visualization. Soft Computing, 24(11), 7977-7987. https://doi.org/10.1007/s00500-019-04083-3
Min, F., Zhu, S., & Wang, Y. (2020). Offline handwritten Chinese character recognition based on improved GoogLeNet. In Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition. https://doi.org/10.1145/3430199.3430202
Misra, D. (2019). Mish: A self regularized non-monotonic activation function. arXiv pre-print arXiv:1908.08681.
Ng, P. E., & Ma, K. K. (2006). A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Transactions on Image Processing, 15(6), 1506-1516. https://doi.org/10.1109/TIP.2005.871129
Patidar, P., Gupta, M., Srivastava, S., & Nagawat, A. K. (2010). Image denoising by various filters for different noise. International Journal of Computer Applications, 9(4), 45-50. https://doi.org/10.5120/1370-1846
Sakarkar, G., Kolekar, M. K. H., Paithankar, K., Patil, G., Dutta, P., Chaturvedi, R., & Kumar, S. (2021). Advance approach for detection of DNS tunneling attack from network packets using deep learning algorithms. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(3), 241-266.
Sakib, S., Ahmed, N., Kabir, A. J., & Ahmed, H. (2019). An overview of convolutional neural network: Its architecture and applications. https://doi.org/10.20944/preprints201811.0546.v4
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, C. L. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2018.00474
Shetti, P. P., & Patil, A. (2017). Performance comparison of mean, median, and Wiener filter in MRI image denoising. International Journal for Research Trends and Innovation, 2, 371-375.
Shi, M., Fujisawa, Y., Wakabayashi, T., & Kimura, F. (2002). Handwritten numeral recognition using gradient and curvature of gray scale image. Pattern Recognition, 35(10), 2051-2059. https://doi.org/10.1016/S0031-3203(01)00203-5
Srinivas, R. & Panda, S. (2013). Performance analysis of various filters for image noise removal in different noise environment. International journal of advanced computer research, 3(4), 47.
Sriporn, K., Tsai, C. F., Tsai, C. E., & Wang, P. (2020). Analyzing malaria disease using effective deep learning approach. Diagnostics, 10(10), 744. https://doi.org/10.3390/diagnostics10100744
Sun, Y. (2021). The role of activation function in image classification. In 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE. https://doi.org/10.1109/CISCE52179.2021.9445868
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2016.308
Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2014.220
Wang, Y., Yang, Y., Ding, W., & Li, S. (2021). A residual-attention offline handwritten Chinese text recognition based on fully convolutional neural networks. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3115606
Xiao, X., Jin, L., Yang, Y., Yang, W., Sun, J., & Chang, T. (2017). Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition. Pattern Recognition, 72, 72-81. https://doi.org/10.1016/j.patcog.2017.06.032
Xu, X., Yang, C., Wang, L., Zhong, J., Bao, W., & Guo, J. (2022). A sophisticated offline network developed for recognizing handwritten Chinese character efficiently. Computers and Electrical Engineering, 100, 107857. https://doi.org/10.1016/j.compeleceng.2022.107857
Yadav, M., Purwar, R. K., & Jain, A. (2018). Design of CNN architecture for Hindi characters. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7(3), 47-61. https://doi.org/10.14201/ADCAIJ2018734762
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