International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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An Enhanced Neural Network Approach for Numeral Recognition

  • Received : 2022.03.05
  • Published : 2022.03.30
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Abstract

Object classification is one of the main fields in neural networks and has attracted the interest of many researchers. Although there have been vast advancements in this area, still there are many challenges that are faced even in the current era due to its inefficiency in handling large data, linguistic and dimensional complexities. Powerful hardware and software approaches in Neural Networks such as Deep Neural Networks present efficient mechanisms and contribute a lot to the field of object recognition as well as to handle time series classification. Due to the high rate of accuracy in terms of prediction rate, a neural network is often preferred in applications that require identification, segmentation, and detection based on features. Neural networks self-learning ability has revolutionized computing power and has its application in numerous fields such as powering unmanned self-driving vehicles, speech recognition, etc. In this paper, the experiment is conducted to implement a neural approach to identify numbers in different formats without human intervention. Measures are taken to improve the efficiency of the machines to classify and identify numbers. Experimental results show the importance of having training sets to achieve better recognition accuracy.

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References

  1. Pai, A. "CNN vs. RNN vs. ANN-analyzing 3 types of neural networks in deep learning." Analytics Vidhya, Feb 17 (2020). https://www.analyticsvidhya.com.
  2. Wu, Jianxin. "Introduction to convolutional neural networks." National Key Lab for Novel Software Technology. Nanjing University. China 5, no. 23 (2017): 495. , https://cs.nju.edu.cn
  3. Learning, Deep. "Ian Goodfellow, Yoshua Bengio, Aaron Courville." The reference book for deep learning models (2016)., http://neuralnetworksanddeeplearning.com
  4. Sadouk, Lamyaa. "CNN approaches for time series classification." In Time Series Analysis-Data, Methods, and Applications, pp. 1-23. IntechOpen, 2019., DOI: 10.5772/intechopen.81170.
  5. LeCun, Yann, Bernhard Boser, John Denker, Donnie Henderson, Richard Howard, Wayne Hubbard, and Lawrence Jackel. "Handwritten digit recognition with a back-propagation network." Advances in neural information processing systems 2 (1989).
  6. Wan, Li, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus. "Regularization of neural networks using dropconnect." In International conference on machine learning, pp. 1058-1066. PMLR, 2013.
  7. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105.
  8. Graves, Alex, and Navdeep Jaitly. "Towards end-to-end speech recognition with recurrent neural networks." In International conference on machine learning, pp. 1764-1772. PMLR, 2014.
  9. Sarikaya, Ruhi, Geoffrey E. Hinton, and Bhuvana Ramabhadran. "Deep belief nets for natural language call-routing." In 2011 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp. 5680-5683. IEEE, 2011.
  10. Smolander, Johannes, Matthias Dehmer, and Frank Emmert-Streib. "Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders." FEBS open bio 9, no. 7 (2019): 1232-1248. https://doi.org/10.1002/2211-5463.12652
  11. Rosenblatt, Frank. "The perceptron: a probabilistic model for information storage and organization in the brain." Psychological review 65, no. 6 (1958): 386. https://doi.org/10.1037/h0042519
  12. Riedmiller, Martin, and Heinrich Braun. "A direct adaptive method for faster backpropagation learning: The RPROP algorithm." In IEEE international conference on neural networks, pp. 586-591. IEEE, 1993.
  13. Fitch, Frederic B. "Warren S. McCulloch and Walter Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of mathematical biophysics, vol. 5 (1943), pp. 115-133." The Journal of Symbolic Logic 9, no. 2 (1944): 49-50. https://doi.org/10.1007/BF02478259
  14. Smalheiser, Neil R. "Walter pitts." Perspectives in Biology and Medicine 43, no. 2 (2000): 217-226. https://doi.org/10.1353/pbm.2000.0009
  15. Hebb, Donald Olding. The organization of behavior: A neuropsychological theory. Psychology Press, 2005.
  16. Rosenblatt, Frank. The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory, 1957.
  17. Kuan, Chung-Ming, Kurt Hornik, and Halbert White. "A convergence result for learning in recurrent neural networks." Neural Computation 6, no. 3 (1994): 420-440. https://doi.org/10.1162/neco.1994.6.3.420
  18. Forssell, Mats. "Hardware implementation of artificial neural networks." Information Flow in Networks 18 (2014): 1-4.
  19. Pradeep, J., E. Srinivasan, and S. Himavathi. "Diagonal based feature extraction for handwritten character recognition system using neural network." In 2011 3rd international conference on electronics computer technology, vol. 4, pp. 364-368. IEEE, 2011..
  20. Sharma, Om Prakash, M. K. Ghose, and Krishna Bikram Shah. "An improved zone based hybrid feature extraction model for handwritten alphabets recognition using euler number." International Journal of Soft Computing and Engineering 2, no. 2 (2012): 504-508.
  21. Suen, C. Y. "Character Recognition by Computer and Applications in Handbook of Pattern Recognition and Image Processing, ed. Young TY, Fu, KS." (1986): 569-586.
  22. Karpathy, Andrej, and Li Fei-Fei. "Deep visual-semantic alignments for generating image descriptions." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3128-3137. 2015.
  23. Bunke, Horst, Samy Bengio, and Alessandro Vinciarelli. "Offline recognition of unconstrained handwritten texts using HMMs and statistical language models." IEEE transactions on Pattern analysis and Machine intelligence 26, no. 6 (2004): 709-720. https://doi.org/10.1109/TPAMI.2004.14
  24. Kozielski, Michal, Patrick Doetsch, and Hermann Ney. "Improvements in rwth's system for off-line handwriting recognition." In 2013 12th International Conference on Document Analysis and Recognition, pp. 935-939. IEEE, 2013.
  25. Bluche, Theodore, Hermann Ney, and Christopher Kermorvant. "Feature extraction with convolutional neural networks for handwritten word recognition." In 2013 12th International Conference on Document Analysis and Recognition, pp. 285-289. IEEE, 2013.
  26. Vivek Singh Bawa, https://pydeeplearning.weebly.com/blog/basic-architecture-of-rnn-and-lstm, Basic architecture of rnn and lstm 1/18/2017, blog.
  27. Hochreiter, Sepp, and Jurgen Schmidhuber. "Long shortterm memory." Neural computation 9, no. 8 (1997): 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  28. Prabhu, Raghav. "Understanding of convolutional neural network (CNN)-deep learning." Medium. Com (2018): 1-11.