Abstract
The handwritten character recognition process has gained significant attention among research communities due to its application in assistive technologies for visually impaired people, human–robot interaction, automated registry for business documents, and so on. Handwritten character recognition of the Telugu language is difficult owing to the absence of a massive dataset and a trained convolutional neural network (CNN). This article introduces an intelligent Telugu character recognition process using a multi-objective mayfly optimization with deep learning (MOMFO-DL) model. The proposed MOMFO-DL technique involves the DenseNet-169 model as a feature extractor to generate a useful set of feature vectors. A functional link neural network (FLNN) is used as a classification model to recognize and classify the printer characters. The design of the MOMFO technique for the parameter optimization of the DenseNet model and FLNN model shows the novelty of the work. The use of MOMFO technique helps to optimally tune the parameters in such a way that the overall performance can be improved. The extensive experimental analysis takes place on benchmark datasets and the outcomes are examined with respect to different measures. The experimental results pointed out the supremacy of the MOMFO technique over the recent state-of-the-art methods.
- [1] . 2020. Handwritten Telugu compound character prediction using convolutional neural network. In International Conference on Emerging Trends in Information Technology and Engineering (IC-ETITE’20). IEEE, 1–4.Google Scholar
Cross Ref
- [2] . 2020. Real-time optimization using reinforcement learning. Computers & Chemical Engineering 143, 107077.Google Scholar
Cross Ref
- [3] . 2021. Self-attention-based conditional random fields latent variables model for sequence labeling. Pattern Recognition Letters 145, 157–164.Google Scholar
Digital Library
- [4] . 2013. A digital character recognition algorithm based on the template weighted match degree. International Journal of Smart Home 7, 3 (2013), 53–60.Google Scholar
- [5] . 2020. Multi-label patent classification using attention-aware deep learning model. In IEEE International Conference on Big Data and Smart Computing (BigComp’20) (558–559). IEEE.Google Scholar
Cross Ref
- [6] A. Galassi, M. Lippi, and P. Torroni. 2021. Attention in natural language processing. IEEE Transactions on Neural Networks and Learning Systems 32, 10 (2021), 4291--4308.Google Scholar
- [7] . 2010. OCR for Telugu script using backpropagation based classifier. International Journal of Information Technology and Knowledge Management 2, 2 (2010), 639–643.Google Scholar
- [8] . 2021. ASRNN: A recurrent neural network with an attention model for sequence labeling. Knowledge-Based Systems 212, 106548.Google Scholar
Cross Ref
- [9] . 2018. Handwritten Devanagari character recognition using layer-wise training of deep convolutional neural networks and adaptive gradient methods. Journal of Imaging 4, 2 (2018), 41.Google Scholar
Cross Ref
- [10] . 2021. Multi-objective neural evolutionary algorithm for combinatorial optimization problems. IEEE Transactions on Neural Networks and Learning Systems.Google Scholar
Cross Ref
- [11] . 2020. T-GSA: Transformer with Gaussian-weighted self-attention for speech enhancement. In ICASSP–IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’20), Electrical Engineering and System Science, 6649--6653.Google Scholar
Cross Ref
- [12] . 2015. Spatial transformer networks. In Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, 11–12.Google Scholar
- [13] . 2021. Performance analysis of telugu characters using deep learning networks. In Advances in Electrical and Computer Technologies: Select Proceedings of ICAECT 2020 (311–322). Springer Singapore.Google Scholar
Cross Ref
- [14] . 2020. DevNet: An efficient CNN architecture for handwritten Devanagari character recognition. International Journal of Pattern Recognition and Artificial Intelligence 34, 12 (2020), 2052009.Google Scholar
Cross Ref
- [15] . 2017. Off-line Telugu handwritten characters recognition using optical character recognition. In International Conference of Electronics, Communication and Aerospace Technology (ICECA’17), 2. IEEE, 223–226.Google Scholar
Cross Ref
- [16] . 2013. An efficient multiclassifier system based on convolutional neural network for offline handwritten Telugu character recognition. InNational Conference on Communications (NCC’13). IEEE, 1–5.Google Scholar
- [17] . 2018. A deep learning approach to recognize handwritten Telugu character using convolution neural networks. International Journal of Information Systems & Management Science 1, 2 (2018).Google Scholar
- [18] . 2021. Deep learning techniques for optical character recognition. In Sustainable Communication Networks and Application. Springer, Singapore, 339–349.Google Scholar
- [19] . 2019. A moment-based representation for online Telugu handwritten character recognition. In Data Analytics and Learning. Springer, Singapore, 27–38.Google Scholar
- [20] . 2021. Multi variant handwritten Telugu character recognition using transfer learning. In IOP Conference Series: Materials Science and Engineering. IOP Publishing, 1042, 1, 012026.Google Scholar
Cross Ref
- [21] . 1989. Adaptive Pattern Recognition and Neural Network. Addison-Wesley, Reading, MA.Google Scholar
Digital Library
- [22] . 2010. A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN. Neural Computing and Applications 19, 2 (2010), 187–205.Google Scholar
Digital Library
- [23] . 2019. Intelligent secure ecosystem based on metaheuristic and functional link neural network for edge of things. IEEE Transactions on Industrial Informatics 16, 3 (2019), 1947–1956.Google Scholar
Cross Ref
- [24] . 2020. A mayfly optimization algorithm. Computers & Industrial Engineering, 145, 106559.Google Scholar
Digital Library
- [25] . 2020. Mayfly Optimization Algorithm (MA): A population-based approach used for the application of flow-shop scheduling problem. Retrieved February 24, 2022 from https://transpireonline.blog/2020/11/11/mayfly-optimization-algorithm-ma-a-population-based-approach-used-for-the-application-of-flow-shop-scheduling-problem/.Google Scholar
- [26] . 2020. Telugu handwritten character dataset. IEEE Dataport,
DOI: Google ScholarCross Ref
- [27] . 2018. UHTelPCC: A dataset for Telugu printed character recognition. In International Conference on Recent Trends in Image Processing and Pattern Recognition. Springer, Singapore, 24–36.Google Scholar
- [28] . 2006. OCR of printed Telugu text with high recognition accuracies. In Computer Vision, Graphics and Image Processing. Springer, Berlin, 786–795.Google Scholar
- [29] . 2004. An intelligent character recognizer for Telugu scripts using multiresolution analysis and associative memory. Image and Vision Computing 22, 14 (2004), 1221–1227.Google Scholar
Cross Ref
- [30] . 2019. Telugu and Hindi script recognition using deep learning techniques. International Journal of Innovative Technology and Exploring Engineering 8, 2278–3075.Google Scholar
Cross Ref
Index Terms
An Intelligent Telugu Handwritten Character Recognition Using Multi-Objective Mayfly Optimization with Deep Learning–Based DenseNet Model
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