Digit Recognition using TensorFlow Tool

K. V. N. Rajesh*, K.V.N. Ramesh**, M. Hymavathi ***, K. Syam Sundar Reddy****
* HOD, Department of Electronics and Computer Engineering, Vignan's Institute of Information Technology, Visakhapatnam, India.
** Technical Architect, Tech Mahindra, Visakhapatnam, India.
***-**** UG Scholar, Department of Information Technology, Vignan's Institute of Information Technology, Visakhapatnam, India.
DOI : https://doi.org/10.26634/jpr.4.3.13887


Deep learning and Neural Network algorithms are a branch of Machine learning that can automatically identify patterns in the data, and then use the uncovered patterns to predict future data, or to perform other alternative kinds of decision making under unreliability. Neural Networks is a method of computing, which is based on a collection of large number of neural units, which acts as a biological brain and solve problems with large clusters of biological neurons connected by axons. Deep Learning algorithms are used to model high level abstractions in data. Digit Recognition is a combination of Deep Learning and Neural Network algorithms, which uses TensorFlow tool as an interface to develop a model. This paper describes the recognition of handwritten scanned digits by a system and displays the output as digital numbers by using Machine Learning methods with the help of TensorFlow tool.


Digit Recognition, TensorFlow, Neural Networks, Deep Learning, Machine Learning

How to Cite this Article?

Rajesh, K. V. N., Ramesh, K. V. N., Hymavathi, M., and Reddy, K. S. S. (2017). Digit Recognition using TensorFlow Tool. i-manager’s Journal on Pattern Recognition, 4(3), 27-31.


[1]. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J. et al. (2016). TensorFlow: A System for Largeth Scale Machine Learning. In 12 USENIX Symposium on Operating Systems Design and Implementation (OSDI'16) (Vol. 16, pp. 265-283).
[2]. Bhattacharya, U., & Chaudhuri, B. B. (2005). Databases for research on recognition of handwritten characters of Indian scripts. In Document Analysis and Recognition, 2005. Proceedings. Eighth International Conference on (pp. 789-793). IEEE.
[3]. Dean, J., & Monga, R. (2015). TensorFlow-Google's latest machine learning system, open sourced for everyone. Google Research Blog.
[4]. Hanmandlu, M., & Murthy, O. R. (2007). Fuzzy model based Recognition of Handwritten Numerals. Pattern Recognition, 40(6), 1840-1854.
[5]. Liu, C. L., Nakashima, K., Sako, H., & Fujisawa, H. (2003). Handwritten Digit Recognition: Benchmarking of state-of-the-art Techniques. Pattern Recognition, 36(10), 2271-2285.
[6]. Plamondon, R., & Srihari, S. N. (2000). Online and offline handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 63-84.
[7]. Suen, C. Y., Berthod, M., & Mori, S. (1980). Automatic recognition of handprinted characters-the state of the art. Proceedings of the IEEE, 68(4), 469-487.
[8]. Sukhaswami, M. B., Seetharamulu, P., & Pujari, A. K. (1995). Recognition of Telugu Characters using Neural Networks. International Journal of Neural Systems, 6(3), 317-357.
[9]. Trier, Ø. D., Jain, A. K., & Taxt, T. (1996). Feature extraction methods for character recognition-a survey. Pattern Recognition, 29(4), 641-662.
[10]. Wongsuphasawat, K., Smilkov, D., Wexler, J., Wilson, J., Mané, D., Fritz, D. et al. (2018). Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow. IEEE Transactions on Visualization and Computer Graphics, 24(1), 1-12.
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