Deep Learning Method for Handwriting Recognition

Year 2021, Volume: 9 Issue: 1, 85 - 92, 30.06.2021

Ayşe Ayvacı Erdoğan , Abdullah Erdal Tümer

https://doi.org/10.51354/mjen.852312

Cited By: 2

Abstract

The advancement of technology nowadays resulted into documents, such as forms and petitions, being filled out in computer and digital environment. Yet in some cases, documents are still preserved in traditional style, on print. Due to its distinct proportions, however, its storage, sharing and filing has become a complication. The relocation of these written documents to digital environment is therefore of great significance. In this view, this study aims to explore methodologies of digitizing handwritten documents. In this study, the documents converted to image format were pre-processed using image processing methods. These operations include dividing lines of the document into image format, dividing into words which then divided into characters, and finally, a classification operation on the characters. As classification phase, one of the deep learning methods is the Convolution Neural Network method is used in image recognition. The model was trained using the EMNIST dataset, and in the character, dataset created from the documents at hand. The dataset created had a success rate of 87.81%. Characters classified as finishers are sequentially combined and the document is transferred to the computer afterwards.

Keywords

Character recognition, convolutional neural network, deep learning, handwriting recognition, image processing

References

• B. Engel-Yeger, L. Nagauker-Yanuv, and S. Rosenblum, “Handwriting performance, self-reports, and perceived self-efficacy among children with dysgraphia,” American Journal of Occupational Therapy, vol. 63, no. 2, pp. 182-192, 2009.
• R. Plamondon, and S. N. Srihari, “Online and off-line handwriting recognition: a comprehensive survey,” IEEE Transactions on pattern analysis and machine intelligence, vol. 22, no. 1, pp. 63-84, 2000.
• S. N. Srihari, “High-performance reading machines,” Proceedings of the IEEE, vol. 80, no. 7, pp. 1120-1132, 1992.
• G. Seni, R. K. Srihari, and N. Nasrabadi, “Large vocabulary recognition of on-line handwritten cursive words,” IEEE Transactions on pattern analysis and machine intelligence, vol. 18, no. 7, pp. 757-762, 1996.
• P. P. Roy, A. K. Bhunia, A. Das et al., “Keyword spotting in doctor's handwriting on medical prescriptions,” Expert Systems with Applications, vol. 76, pp. 113-128, 2017.
• S. N. Srihari, S.-H. Cha, H. Arora et al., “Handwriting identification: Research to study validity of individuality of handwriting and develop computer-assisted procedures for comparing handwriting,” Technical Report CEDAR-TR-01-1, 2001.
• M. I. Fanany, "Handwriting recognition on form document using convolutional neural network and support vector machines (CNN-SVM)." pp. 1-6.
• D. Mahapatra, C. Choudhury, and R. K. Karsh, "Handwritten Character Recognition Using KNN and SVM Based Classifier over Feature Vector from Autoencoder." pp. 304-317.
• P. Saha, and A. Jaiswal, "Handwriting Recognition Using Active Contour," Artificial Intelligence and Evolutionary Computations in Engineering Systems, pp. 505-514: Springer, 2020.
• S. A. Gregory Cohen, Jonathan Tapson, and Andre van Schaik, “EMNIST: an extension of MNIST to handwritten letters,” 2017.
• B. Baykal, T. Ö. Aktaş, and O. Yildiz, "Makİne Öğrenmesİ yÖntemlerİ İle tomatİk ÇevrİmdiŞi İmza tanima ve doğrulama Sistemİ." pp. 1-5.
• B. Sun, L. Yang, W. Zhang et al., "Demonstration of Applications in Computer Vision and NLP on Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3 TOPS/Watt." pp. 611-611.
• L. Akhtyamova, A. Ignatov, and J. Cardiff, "A Large-scale CNN ensemble for medication safety analysis." pp. 247-253.
• M. Cho, J. Ha, C. Park et al., “Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition,” Journal of Biomedical Informatics, vol. 103, pp. 103381, 2020.
• A. Momeni, M. Thibault, and O. Gevaert, "Dropout-enabled ensemble learning for multi-scale biomedical data." pp. 407-415.
• M. Amin-Naji, A. Aghagolzadeh, and M. Ezoji, “Ensemble of CNN for multi-focus image fusion,” Information fusion, vol. 51, pp. 201-214, 2019.
• C. Tian, Y. Xu, and W. Zuo, “Image denoising using deep CNN with batch renormalization,” Neural Networks, vol. 121, pp. 461-473, 2020.
• Z. Mushtaq, S.-F. Su, and Q.-V. Tran, “Spectral images based environmental sound classification using CNN with meaningful data augmentation,” Applied Acoustics, vol. 172, pp. 107581, 2020.
• Y. Su, K. Zhang, J. Wang et al., “Environment sound classification using a two-stream CNN based on decision-level fusion,” Sensors, vol. 19, no. 7, pp. 1733, 2019.
• T. Ergin, “Convolutional Neural Network (ConvNet yada CNN) nedir, nasıl çalışır? ,” 2018,October