How to cite this paper
Hamad, H & Shehab, M. (2024). Integrated multi-layer perceptron neural network and novel feature extraction for handwritten Arabic recognition.International Journal of Data and Network Science, 8(3), 1501-1516.
Refrences
Ahmed, R., Gogate, M., Tahir, A., Dashtipour, K., Al-Tamimi, B., Hawalah, A., ... & Hussain, A. (2021). Novel deep con-volutional neural network-based contextual recognition of Arabic handwritten scripts. Entropy, 23(3), 340.
Alfaro-Contreras, M., Ríos-Vila, A., Valero-Mas, J. J., & Calvo-Zaragoza, J. (2023). Few-shot symbol classification via self-supervised learning and nearest neighbor. Pattern Recognition Letters, 167, 1-8.
Al Hamad, H. A. (2012). Over-segmentation of handwriting Arabic scripts using an efficient heuristic technique. In 2012 International Conference on Wavelet Analysis and Pattern Recognition (pp. 180-185). IEEE.
Al Hamad, H. A., Abualigah, L., Shehab, M., Al-Shqeerat, K. H., & Otair, M. (2022). Improved linear density technique for segmentation in Arabic handwritten text recognition. Multimedia Tools and Applications, 81(20), 28531-28558.
Al Hamad, H. A. (2013a). Use an efficient neural network to improve the Arabic handwriting recognition. Signal and Im-age Processing Applications (ICSIPA). In 2013 IEEE International Conference on. IEEE, 269–274.
Al Hamad, H. A. (2013b). Neural-based segmentation technique for Arabic handwriting scripts. in Proc. 21st Internation-al Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EU-ROGRAPHICS Association, West Bohemia, Czech Republic, pp. 9-14.
Al Hamad, H. A. (2015). Skew detection/correction and local minima/maxima techniques for extracting a new Arabic benchmark database. International Journal of Advanced Computer Science and Applications (IJACSA), 6(9), 1-10.
Ali, A. A., & Mallaiah, S. (2022). Intelligent handwritten recognition using hybrid CNN architectures based-SVM classi-fier with dropout. Journal of King Saud University-Computer and Information Sciences, 34(6), 3294-3300.
Altay, O., & Varol Altay, E. (2023). A novel hybrid multilayer perceptron neural network with improved grey wolf opti-mizer. Neural Computing and Applications, 35(1), 529-556.
Balaha, H. M., Ali, H. A., Saraya, M., & Badawy, M. (2021). A new Arabic handwritten character recognition deep learn-ing system (AHCR-DLS). Neural Computing and Applications, 33, 6325-6367.
Dar, K. S., Shafat, A. B., & Hassan, M. U. (2017, June). An efficient stop word elimination algorithm for Urdu lan-guage. In 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunica-tions and Information Technology (ECTI-CON) (pp. 911-914). IEEE.
Durga, L., & Deepu, R. (2021). Ensemble deep learning to classify specific types of t and i patterns in graphology. Global Transitions Proceedings, 2(2), 287-293.
Ehteram, M., Ahmed, A. N., Ling, L., Fai, C. M., Latif, S. D., Afan, H. A., ... & El-Shafie, A. (2020). Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm. Water, 12(3), 902.
Ghadhban, H. Q., Othman, M., Samsudin, N. A., Ismail, M. N. B., & Hammoodi, M. R. (2020). Survey of offline Arabic handwriting word recognition. In Recent Advances on Soft Computing and Data Mining: Proceedings of the Fourth In-ternational Conference on Soft Computing and Data Mining (SCDM 2020), Melaka, Malaysia, 358-372.
Ghanim, T. M., Khalil, M. I., & Abbas, H. M. (2020). Comparative study on deep convolution neural networks DCNN-based offline Arabic handwriting recognition. IEEE Access, 8, 95465-95482.
Guellil, I., Saâdane, H., Azouaou, F., Gueni, B., & Nouvel, D. (2021). Arabic natural language processing: An overview. Journal of King Saud University-Computer and Information Sciences, 33(5), 497-507. doi: 10.1016/j.jksuci.2019.02.006
Gupta, D., & Bag, S. (2021). CNN-based multilingual handwritten numeral recognition: a fusion-free approach. Expert Systems with Applications, 165, 113784.
Haq, M. U., Sethi, M. A. J., & Rehman, A. U. (2023). Capsule Network with Its Limitation, Modification, and Applica-tions—A Survey. Machine Learning and Knowledge Extraction, 5(3), 891-921.
Kavitha, B. R., & Srimathi, C. B. (2022). Benchmarking on offline Handwritten Tamil Character Recognition using con-volutional neural networks. Journal of King Saud University-Computer and Information Sciences, 34(4), 1183-1190.
Maalej, R., & Kherallah, M. (2022). New MDLSTM-based designs with data augmentation for offline Arabic handwriting recognition. Multimedia Tools and Applications, 81(7), 10243-10260.
Meddeb, O., Maraoui, M., & Zrigui, M. (2021). Arabic text documents recommendation using joint deep representations learning. Procedia Computer Science, 192, 812-821.
Moreno-Barea, F. J., Jerez, J. M., & Franco, L. (2020). Improving classification accuracy using data augmentation on small data sets. Expert Systems with Applications, 161, 113696.
Moudgil, A., Singh, S., Gautam, V., Rani, S., & Shah, S. H. (2023). Handwritten devanagari manuscript characters recog-nition using capsnet. International Journal of Cognitive Computing in Engineering, 4, 47-54.
Nahar, K. M., Alsmadi, I., Al Mamlook, R. E., Nasayreh, A., Gharaibeh, H., Almuflih, A. S., & Alasim, F. (2023). Recog-nition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques. Sensors, 23(23), 9475.
Pareek, J., Singhania, D., Kumari, R. R., & Purohit, S. (2020). Gujarati handwritten character recognition from text images. Procedia Computer Science, 171, 514-523.
Peng, D., Jin, L., Ma, W., Xie, C., Zhang, H., Zhu, S., & Li, J. (2022). Recognition of handwritten Chinese text by segmen-tation: a segment-annotation-free approach. IEEE Transactions on Multimedia, 25, 2368-2381
Prieto, J. R., Andrés, J., Granell, E., Sánchez, J. A., & Vidal, E. (2023). Information extraction in handwritten historical logbooks. Pattern Recognition Letters, 172, 128-136.
Rabi, M., Amrouch, M., & Mahani, Z. (2018). Recognition of cursive Arabic handwritten text using embedded training based on hidden Markov models. International journal of pattern recognition and Artificial Intelligence, 32(01), 1860007.
Shuvo, M. I. R., Akhand, M. A. H., & Siddique, N. (2022). Handwritten numeral recognition through superimposition onto printed form. Journal of King Saud University-Computer and Information Sciences, 34(9), 7751-7764.
Singh, S., Sharma, A., & Chauhan, V. K. (2021). Online handwritten Gurmukhi word recognition using fine-tuned deep convolutional neural network on offline features. Machine Learning with Applications, 5, 100037.
Song, X., Cong, Y., Song, Y., Chen, Y., & Liang, P. (2022). A bearing fault diagnosis model based on CNN with wide con-volution kernels. Journal of Ambient Intelligence and Humanized Computing, 13(8), 4041-4056.
Souibgui, M. A., Fornés, A., Kessentini, Y., & Megyesi, B. (2022). Few shots are all you need: a progressive learning ap-proach for low resource handwritten text recognition. Pattern Recognition Letters, 160, 43-49.
Zhang, T., Chen, J., Li, F., Zhang, K., Lv, H., He, S., & Xu, E. (2022). Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions. ISA transactions, 119, 152-171.
Alfaro-Contreras, M., Ríos-Vila, A., Valero-Mas, J. J., & Calvo-Zaragoza, J. (2023). Few-shot symbol classification via self-supervised learning and nearest neighbor. Pattern Recognition Letters, 167, 1-8.
Al Hamad, H. A. (2012). Over-segmentation of handwriting Arabic scripts using an efficient heuristic technique. In 2012 International Conference on Wavelet Analysis and Pattern Recognition (pp. 180-185). IEEE.
Al Hamad, H. A., Abualigah, L., Shehab, M., Al-Shqeerat, K. H., & Otair, M. (2022). Improved linear density technique for segmentation in Arabic handwritten text recognition. Multimedia Tools and Applications, 81(20), 28531-28558.
Al Hamad, H. A. (2013a). Use an efficient neural network to improve the Arabic handwriting recognition. Signal and Im-age Processing Applications (ICSIPA). In 2013 IEEE International Conference on. IEEE, 269–274.
Al Hamad, H. A. (2013b). Neural-based segmentation technique for Arabic handwriting scripts. in Proc. 21st Internation-al Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EU-ROGRAPHICS Association, West Bohemia, Czech Republic, pp. 9-14.
Al Hamad, H. A. (2015). Skew detection/correction and local minima/maxima techniques for extracting a new Arabic benchmark database. International Journal of Advanced Computer Science and Applications (IJACSA), 6(9), 1-10.
Ali, A. A., & Mallaiah, S. (2022). Intelligent handwritten recognition using hybrid CNN architectures based-SVM classi-fier with dropout. Journal of King Saud University-Computer and Information Sciences, 34(6), 3294-3300.
Altay, O., & Varol Altay, E. (2023). A novel hybrid multilayer perceptron neural network with improved grey wolf opti-mizer. Neural Computing and Applications, 35(1), 529-556.
Balaha, H. M., Ali, H. A., Saraya, M., & Badawy, M. (2021). A new Arabic handwritten character recognition deep learn-ing system (AHCR-DLS). Neural Computing and Applications, 33, 6325-6367.
Dar, K. S., Shafat, A. B., & Hassan, M. U. (2017, June). An efficient stop word elimination algorithm for Urdu lan-guage. In 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunica-tions and Information Technology (ECTI-CON) (pp. 911-914). IEEE.
Durga, L., & Deepu, R. (2021). Ensemble deep learning to classify specific types of t and i patterns in graphology. Global Transitions Proceedings, 2(2), 287-293.
Ehteram, M., Ahmed, A. N., Ling, L., Fai, C. M., Latif, S. D., Afan, H. A., ... & El-Shafie, A. (2020). Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm. Water, 12(3), 902.
Ghadhban, H. Q., Othman, M., Samsudin, N. A., Ismail, M. N. B., & Hammoodi, M. R. (2020). Survey of offline Arabic handwriting word recognition. In Recent Advances on Soft Computing and Data Mining: Proceedings of the Fourth In-ternational Conference on Soft Computing and Data Mining (SCDM 2020), Melaka, Malaysia, 358-372.
Ghanim, T. M., Khalil, M. I., & Abbas, H. M. (2020). Comparative study on deep convolution neural networks DCNN-based offline Arabic handwriting recognition. IEEE Access, 8, 95465-95482.
Guellil, I., Saâdane, H., Azouaou, F., Gueni, B., & Nouvel, D. (2021). Arabic natural language processing: An overview. Journal of King Saud University-Computer and Information Sciences, 33(5), 497-507. doi: 10.1016/j.jksuci.2019.02.006
Gupta, D., & Bag, S. (2021). CNN-based multilingual handwritten numeral recognition: a fusion-free approach. Expert Systems with Applications, 165, 113784.
Haq, M. U., Sethi, M. A. J., & Rehman, A. U. (2023). Capsule Network with Its Limitation, Modification, and Applica-tions—A Survey. Machine Learning and Knowledge Extraction, 5(3), 891-921.
Kavitha, B. R., & Srimathi, C. B. (2022). Benchmarking on offline Handwritten Tamil Character Recognition using con-volutional neural networks. Journal of King Saud University-Computer and Information Sciences, 34(4), 1183-1190.
Maalej, R., & Kherallah, M. (2022). New MDLSTM-based designs with data augmentation for offline Arabic handwriting recognition. Multimedia Tools and Applications, 81(7), 10243-10260.
Meddeb, O., Maraoui, M., & Zrigui, M. (2021). Arabic text documents recommendation using joint deep representations learning. Procedia Computer Science, 192, 812-821.
Moreno-Barea, F. J., Jerez, J. M., & Franco, L. (2020). Improving classification accuracy using data augmentation on small data sets. Expert Systems with Applications, 161, 113696.
Moudgil, A., Singh, S., Gautam, V., Rani, S., & Shah, S. H. (2023). Handwritten devanagari manuscript characters recog-nition using capsnet. International Journal of Cognitive Computing in Engineering, 4, 47-54.
Nahar, K. M., Alsmadi, I., Al Mamlook, R. E., Nasayreh, A., Gharaibeh, H., Almuflih, A. S., & Alasim, F. (2023). Recog-nition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques. Sensors, 23(23), 9475.
Pareek, J., Singhania, D., Kumari, R. R., & Purohit, S. (2020). Gujarati handwritten character recognition from text images. Procedia Computer Science, 171, 514-523.
Peng, D., Jin, L., Ma, W., Xie, C., Zhang, H., Zhu, S., & Li, J. (2022). Recognition of handwritten Chinese text by segmen-tation: a segment-annotation-free approach. IEEE Transactions on Multimedia, 25, 2368-2381
Prieto, J. R., Andrés, J., Granell, E., Sánchez, J. A., & Vidal, E. (2023). Information extraction in handwritten historical logbooks. Pattern Recognition Letters, 172, 128-136.
Rabi, M., Amrouch, M., & Mahani, Z. (2018). Recognition of cursive Arabic handwritten text using embedded training based on hidden Markov models. International journal of pattern recognition and Artificial Intelligence, 32(01), 1860007.
Shuvo, M. I. R., Akhand, M. A. H., & Siddique, N. (2022). Handwritten numeral recognition through superimposition onto printed form. Journal of King Saud University-Computer and Information Sciences, 34(9), 7751-7764.
Singh, S., Sharma, A., & Chauhan, V. K. (2021). Online handwritten Gurmukhi word recognition using fine-tuned deep convolutional neural network on offline features. Machine Learning with Applications, 5, 100037.
Song, X., Cong, Y., Song, Y., Chen, Y., & Liang, P. (2022). A bearing fault diagnosis model based on CNN with wide con-volution kernels. Journal of Ambient Intelligence and Humanized Computing, 13(8), 4041-4056.
Souibgui, M. A., Fornés, A., Kessentini, Y., & Megyesi, B. (2022). Few shots are all you need: a progressive learning ap-proach for low resource handwritten text recognition. Pattern Recognition Letters, 160, 43-49.
Zhang, T., Chen, J., Li, F., Zhang, K., Lv, H., He, S., & Xu, E. (2022). Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions. ISA transactions, 119, 152-171.