Call for Paper - Upcoming Issues

A Novel Approach: CNN-RNN and Bi-GRU for Handwritten Character Recognition

International Journal of Electronics and Communication Engineering
© 2024 by SSRG - IJECE Journal
Volume 11 Issue 1
Year of Publication : 2024
Authors : Sunitha S. Nair, P. Ranjit Jeba Thangaiah
10.14445/23488549/IJECE-V11I1P101
pdf
How to Cite?

Sunitha S. Nair, P. Ranjit Jeba Thangaiah, "A Novel Approach: CNN-RNN and Bi-GRU for Handwritten Character Recognition," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 1, pp. 1-14, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I1P101

Abstract:

Handwritten Character Recognition (HCR) plays a crucial role in converting handwritten content into machinereadable format with applications spanning various sectors such as historical document digitization, postal envelope address reading, form processing, and assisting individuals with disabilities. Recognizing handwritten characters is inherently challenging due to the diversity in writing styles and shapes and the presence of noise in the data. Recent advances in deep learning, particularly the utilization of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have greatly enhanced the accuracy and performance of HCR systems. The integration of a CNN-RNN model with Bidirectional Gated Recurrent Units (Bi-GRU) has shown great promise, achieving an impressive accuracy rate of 96.72%. The CNN component excels at capturing spatial features and character structures, while the RNN with Bi-GRU layers effectively models sequential dependencies in handwritten text. As technology continues to advance and more data becomes available, the future holds the promise of even more refined and powerful HCR models. This hybrid approach has the potential to automate processes, enhance data processing, and improve user experiences in a wide range of industries.

Keywords:

Character shapes, Deep Learning, Handwritten data, Optical Character Recognition, Sequential dependencies.

References:

[1] Charles A. Perfetti, and Susan Dunlap, Learning to Read: General Principles and Writing System Variations, Learning to Read across Languages, Routledge, pp. 25-50, 2008.
[Google Scholar] [Publisher Link]
[2] Umapada Pal, Ramachandran Jayadevan, and Nabin Sharma, “Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques,” ACM Transactions on Asian Language Information Processing, vol. 11, no. 1, pp. 1-35, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Paul Westwood, and Gary Crew, “The Future of Handwriting,” Australian Association of Writing Programs, vol. 22, no. 52, pp. 1-10, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] M. Swamy Das, D. Sandhya Rani, and C.R.K. Reddy, “Heuristic Based Script Identification from Multilingual Text Documents,” 2012 1st International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, India, pp. 487-492, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[5] J. Mantas, “An Overview of Character Recognition Methodologies,” Pattern Recognition, vol. 19, no. 6, pp. 425-430, 1986.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Jamshed Memon et al., “Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR),” IEEE Access, vol. 8, pp. 142642-142668, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] K. Yoshida, and H. Sakoe, “Online Handwritten Character Recognition for a Personal Computer System,” IEEE Transactions on Consumer Electronics, vol. CE-28, no. 3, pp. 202-209, 1982.
[CrossRef] [Google Scholar] [Publisher Link]
[8] N. Arica, and F.T. Yarman-Vural, “An Overview of Character Recognition Focused on Off-Line Handwriting,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 31, no. 2, pp. 216-233, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Amitha Mathew, P. Amudha, and S. Sivakumari, “Deep Learning Techniques: An Overview,” Advanced Machine Learning Technologies and Applications, pp. 599-608, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] K.R. Chowdhary, “Natural Language Processing,” Fundamentals of Artificial Intelligence, pp. 603-649, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yasir Babiker Hamdan, and A. Sathesh, “Construction of Statistical SVM Based Recognition Model for Handwritten Character Recognition,” Journal of Information Technology and Digital World, vol. 3, no. 2, pp. 92-107, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] I. Khandokar et al., “Handwritten Character Recognition Using Convolutional Neural Network,” Journal of Physics: Conference Series, vol. 1918, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Saleh Albahli et al., “An Improved Faster-RCNN Model for Handwritten Character Recognition,” Arabian Journal for Science and Engineering, vol. 46, no. 9, pp. 8509-8523, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Riya Guha et al., “Devnet: An Efficient CNN Architecture for Handwritten Devanagari Character Recognition,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 34, no. 12, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Mayur Bhargab Bora et al., “Handwritten Character Recognition from Images Using CNN-ECOC,” Procedia Computer Science, vol. 167, pp. 2403-2409, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Zhiyuan Li et al., “Deep Matching Network for Handwritten Chinese Character Recognition,” Pattern Recognition, vol. 107, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Jyoti Pareek et al., “Gujarati Handwritten Character Recognition from Text Images,” Procedia Computer Science, vol. 171, pp. 514-523, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] N. Shobha Rani et al., “Deep Learning Network Architecture-Based Kannada Handwritten Character Recognition,” 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, pp. 213-220, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Hossam Magdy Balaha et al., “A New Arabic Handwritten Character Recognition Deep Learning System (AHCR-DLS),” Neural Computing and Applications, vol. 33, pp. 6325-6367, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Yu Weng, and Chunlei Xia, “A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices,” Mobile Networks and Applications, vol. 25, pp. 402-411, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Pavlo Melnyk, Zhiqiang You, and Keqin Li, “A High-Performance CNN Method for Offline Handwritten Chinese Character Recognition and Visualization,” Soft Computing, vol. 24, no. 11, pp. 7977-7987, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Ferdin Joe John Joseph, “Effect of Supervised Learning Methodologies in Offline Handwritten Thai Character Recognition,” International Journal of Information Technology, vol. 12, no. 1, pp. 57-64, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Tapotosh Ghosh et al., “Bangla Handwritten Character Recognition Using MobileNet V1 Architecture,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 6, pp. 2547-2554, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Rohan Sethi, and Ila Kaushik, “Hand Written Digit Recognition Using Machine Learning,” 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT), Gwalior, India, pp. 49-54, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Wenfei Liu, Jingcheng Wei, and Qingmin Meng, “Comparisions on KNN, SVM, BP and the CNN for Handwritten Digit Recognition,” 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China, pp. 587-590, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Anita Pal, and Dayashankar Singh, “Handwritten English Character Recognition Using Neural Network,” International Journal of Computer Science & Communication, vol. 1, no. 2, pp. 141-144, 2010.
[Google Scholar] [Publisher Link]
[27] Md. Mahbubar Rahman et al., “Bangla Handwritten Character Recognition Using Convolutional Neural Network,” International Journal of Image, Graphics and Signal Processing, vol. 7, no. 8, pp. 42-49, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Aiquan Yuan et al., “Offline Handwritten English Character Recognition Based on Convolutional Neural Network,” 2012 10th IAPR International Workshop on Document Analysis Systems, Gold Coast, Australia, pp. 125-129, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Ahmed El-Sawy, Mohamed Loey, and Hazem EL-Bakry, “Arabic Handwritten Characters Recognition Using Convolutional Neural Network,” WSEAS Transactions on Computer Research, vol. 5, no. 1, pp. 11-19, 2017.
[Google Scholar] [Publisher Link]
[30] N. Shanthi, and K. Duraiswamy, “A Novel SVM-Based Handwritten Tamil Character Recognition System,” Pattern Analysis and Applications, vol. 13, pp. 173-180, 2010.
[CrossRef] [Google Scholar] [Publisher Link]