175
Views
0
CrossRef citations to date
0
Altmetric
Computer Science

Nearest quad-tree (NQT) classifier: a novel framework for handwritten character recognition

ORCID Icon, , , , &
Article: 2347363 | Received 11 Feb 2024, Accepted 18 Apr 2024, Published online: 10 May 2024

References

  • AlKendi, W., Gechter, F., Heyberger, L., & Guyeux, C. (2024). Advancements and challenges in handwritten text recognition: A comprehensive survey. Journal of Imaging, 10(1), 18. https://doi.org/10.3390/JIMAGING10010018
  • Alom, M. Z., Sidike, P., Hasan, M., Taha, T. M., & Asari, V. K. (2018). Handwritten bangla character recognition using the state-of-the-art deep convolutional neural networks. Computational Intelligence and Neuroscience, 2018, 6747098–6747013. https://doi.org/10.1155/2018/6747098
  • Ashlin Deepa, R. N., & Rajeswara Rao, R. (2020). A novel nearest interest point classifier for offline Tamil handwritten character recognition. Pattern Analysis and Applications, 23(1), 199–212. https://doi.org/10.1007/s10044-018-00776-x
  • Ashlin, R. N., Deepa, S., Narayanan, A., Padthe, & Ramannavar, M. (2023). A reduced feature-set OCR system to recognize handwritten tamil characters using SURF local descriptor. International Journal of Advanced Computer Science and Applications, 14(10), 331–344. https://doi.org/10.14569/IJACSA.2023.0141036
  • Bappi, J. O., & Abu, M. (2024). CBD2023: A hypercomplex bangla handwriting character recognition data for hierarchical class expansion using deep learning. Data in Brief, 52, 109909–109909. https://doi.org/10.1016/j.dib.2023.109909
  • Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359. https://doi.org/10.1016/j.cviu.2007.09.014
  • Chen, C., Zhang, P., Zhang, H., Dai, J., Yi, Y., Zhang, H., & Zhang, Y. (2020). Deep learning on computational-resource-limited platforms: A survey. Mobile Information Systems, 2020, 1–19. https://doi.org/10.1155/2020/8454327
  • Deore, S. P., & Pravin, A. (2020). Devanagari handwritten character recognition using fine-tuned deep convolutional neural network on trivial dataset. Sādhanā, 45(1), 2020. https://doi.org/10.1007/s12046-020-01484-1
  • Habib, G., & Qureshi, S. (2020). Optimization and acceleration of convolutional neural networks: A survey. Journal of King Saud University - Computer and Information Sciences, 34(7), 4244–4268. https://doi.org/10.1016/j.jksuci.2020.10.004
  • Isolated Handwritten Tamil Character Dataset. (2006). https://lipitk.sourceforge.net/datasets/tamilchardata.htm, June 2006.
  • James, A., Manjusha, J., & Saravanan, C. (2018). Malayalam handwritten character recognition using AlexNet based architecture. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 6(4). https://doi.org/10.11591/ijeei.v6i4.518
  • Jangid, M., & Srivastava, S. (2018). Handwritten devanagari character recognition using layer-wise training of deep convolutional neural networks and adaptive gradient methods. Journal of Imaging, 4(2), 41. https://doi.org/10.3390/jimaging4020041
  • 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.
  • Kowsalya, S., & Periasamy, P. S. (2019). Recognition of Tamil handwritten character using modified neural network with aid of elephant herding optimization. Multimedia Tools and Applications, 78(17), 25043–25061. https://doi.org/10.1007/s11042-019-7624-2
  • Kumar, M., Jindal, M. K., & Sharma, R. K. (2011). Review on OCR for handwritten Indian scripts character recognition. Communications in computer and information science (pp. 268–276). Springer, Berlin, Heidelberg.
  • Lamrini, M., Chkouri, M. Y., & Touhafi, A. (2023). Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded systems for anomaly detection in smart cities. Sensors, 23(13), 6227–6227. https://doi.org/10.3390/s23136227
  • Lawrence, T., & Zhang, L. (2019). IoTNet: An efficient and accurate convolutional neural network for IoT devices. Sensors, 19(24), 5541. https://doi.org/10.3390/s19245541
  • Li, H., Wang, Z., Yue, X., Wang, W., Tomiyama, H., & Meng, L. (2023). An architecture-level analysis on deep learning models for low-impact computations. Artificial Intelligence Review, 56(3), 1971–2010. https://doi.org/10.1007/s10462-022-10221-5
  • Liang, L., & Ke, Y. (2023). User behavior data analysis and product design optimization algorithm based on deep learning. International Journal on Interactive Design and Manufacturing (IJIDeM). https://doi.org/10.1007/s12008-023-01652-7
  • Memon, J., Sami, M., Khan, R. A., & Uddin, M. (2020). Handwritten optical character recognition (OCR): A comprehensive systematic literature review (SLR). IEEE Access, 8, 142642–142668. https://doi.org/10.1109/ACCESS.2020.3012542
  • Meng, C., Trinh, L., Xu, N., Enouen, J., & Liu, Y. (2022). Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset. Scientific Reports, 12(1), 7166. https://doi.org/10.1038/s41598-022-11012-2
  • Moudgil, A., Singh, S., Gautam, V., Rani, S., & Shah, S. H. (2023). Handwritten Devanagari manuscript characters recognition using capsnet. International Journal of Cognitive Computing in Engineering, 4, 47–54. https://doi.org/10.1016/j.ijcce.2023.02.001
  • Padma, M. C., & Pasha, S. (2014). Quadtree based feature extraction technique for recognizing handwritten Kannada characters. Lecture Notes in Electrical Engineering, 248:725–735. https://doi.org/10.1007/978-81-322-1157-0_74
  • Pal, U., Jayadevan, R., & Sharma, N. (2012). Handwriting recognition in Indian regional scripts. ACM Transactions on Asian Language Information Processing, 11(1), 1–35. https://doi.org/10.1145/2090176.2090177
  • Pal, U., Sharma, N., Wakabayashi, T., & Kimura, F. (2008). Handwritten character recognition of popular South Indian scripts (pp. 251–264). Springer eBooks.
  • Parihar, G., Rajalakshmi, R., & Bhuvana, J. (2021). Multi‐lingual handwritten character recognition using deep learning (pp.155–180).
  • Poornima Devi, M., & Sornam, M. (2020). Classification of ancient handwritten Tamil characters on palm leaf inscription using modified adaptive backpropagation neural network with GLCM features. ACM Transactions on Asian and Low-Resource Language Information Processing, 19(6), 1–24. https://doi.org/10.1145/3406209
  • Pragathi, M. A., Priyadarshini, K., Saveetha, S., Banu, A. S., & Mohammed Aarif, K. O. (2019 Handwritten tamil character recognition using deep learning [Paper presentation]. 2019 International Conference on Vision towards Emerging Trends in Communication and Networking (ViTECoN) (pp. 1–5), Vellore, India. https://doi.org/10.1109/ViTECoN.2019.8899614
  • Saqib, N., Haque, K. F., Yanambaka, V. P., & Abdelgawad, A. (2022). Convolutional-neural-network-based handwritten character recognition: An approach with massive multisource data. Algorithms, 15(4), 129. https://doi.org/10.3390/a15040129
  • Sarkhel, R., Das, N., Das, A., Kundu, M., & Nasipuri, M. (2017). A multi-scale deep quad tree based feature extraction method for the recognition of isolated handwritten characters of popular indic scripts. Pattern Recognition, 71, 78–93. https://doi.org/10.1016/j.patcog.2017.05.022
  • Sheikhalishahi, S., Bhattacharyya, A., Celi, L. A., & Osmani, V. (2023). An interpretable deep learning model for time-series electronic health records: Case study of delirium prediction in critical care. Artificial Intelligence in Medicine, 144, 102659. https://doi.org/10.1016/j.artmed.2023.102659
  • Singh, P. K., Das, S., Sarkar, R., & Nasipuri, M. (2020). A new approach for texture based script identification at block level using quad tree decomposition. https://arxiv.org/abs/2009.07435.
  • Taye, M. M. (2023). Understanding of machine learning with deep learning: Architectures, workflow, applications and future directions. Computers, 12(5), 91–91. https://doi.org/10.3390/computers12050091
  • Vinjit, B. M., Bhojak, M. K., Kumar, S., & Chalak, G. (2020 A review on handwritten character recognition methods and techniques [Paper presentation]. 2020 International Conference on Communication and Signal Processing (ICCSP) (pp. 1224–1228). Chennai, India. https://doi.org/10.1109/ICCSP48568.2020.9182129
  • Wang, X. F., He, Z. H., Wang, K., Wang, Y. F., Zou, L., & Wu, Z. Z. (2023). A survey of text detection and recognition algorithms based on deep learning technology. Neurocomputing, 556, 126702. https://doi.org/10.1016/j.neucom.2023.126702
  • Weng, Y., & Xia, C. (2019). A new deep learning-based handwritten character recognition system on mobile computing devices. Mobile Networks and Applications, 25(2), 402–411. https://doi.org/10.1007/s11036-019-01243-5
  • Wu, C., Fan, W., He, Y., Sun, J., & Naoi, S. (2014). Handwritten character recognition by alternately trained relaxation convolutional neural network. 2014 14th international conference on frontiers in handwriting recognition (pp. 291–296).