Handwritten Character Recognition using Convolution Neural Networks in Python with Keras

  • Hanu Priya Indiran
Keywords: Computer Vision , CNN, Character Recognition, Classification, Deep Learning, Neural Networks


In the field of Deep Learning for Computer Vision, scientists have made many enhancements that helped a lot in the development of millions of smart devices. On the other hand, scientists brought a revolutionary change in the field of image processing and one of the biggest challenges in it is to identify documents in both printed as well as hand-written formats. One of the most widely used techniques for the validity of these types of documents is ‘Character Recognition’. This project seeks to classify an individual handwritten word so that handwritten text can be translated to a digital form. It demonstrates the use of neural networks for developing  a  system  that  can  recognize  handwritten  English alphabets. In this system, each English alphabet is represented by  binary  values  that are  used  as  input to  a  simple  feature extraction  system, whose  output is  fed to  our neural  network system. The CNN approach is used to accomplish this task: classifying words directly and character segmentation. For the former, Convolutional Neural Network (CNN) is used with various architectures to train a model that can accurately classify words. For the latter, Long Short Term Memory networks  are used with convolution to construct bounding boxes for each character. We then pass the segmented characters to a CNN for classification, and then reconstruct each word according to the results of classification and segmentation. 


Download data is not yet available.


[1] H. Al-Yousefi and S. S. Udpa, "Recognition of handwritten Arabic characters," in Proc. SPIE 32nd Ann. Int. Tech. Symp. Opt. Optoelectric Applied Sci. Eng. (San Diego, CA), Aug. 1988.

[2] K. Badi and M. Shimura, "Machine recognition of Arabic cursive script" Trans. Inst. Electron. Commun. Eng., Vol. E65, no. 2, pp. 107-114, Feb. 1982.

[3] M Altuwaijri , M.A Bayoumi , "Arabic Text Recognition Using Neural Network" ISCAS 94. IEEE International Symposium on Circuits and systems, Volume 6, 30 May-2 June 1994.

[4] C. Bahlmann, B. Haasdonk, H. Burkhardt., “Online Handwriting Recognition with Support Vector Machine – A Kernel Approach”, In proceeding of the 8th Int. Workshop in Handwriting Recognition (IWHFR), pp 49- 54, 2002

[5] Homayoon S.M. Beigi, "An Overview of Handwriting Recognition," Proceedings of the 1st Annual Conference on Technological Advancements in Developing Countries, Columbia University, New York, July 24-25, 1993, pp. 30- 46.

[6] Nadal, C. Legault, R. Suen and C.Y, “Complementary Algorithms for Recognition of totally Unconstrained Handwritten Numerals,” in Proc. 10th Int. Conf. Pattern Recognition, 1990, vol. 1, pp. 434-449.

[7] S. Impedovo, P. Wang, and H. Bunke, editors, “Automatic Bankcheck Processing,” World Scientific, Singapore, 1997.

[8] CL Liu, K Nakashima, H Sako and H. Fujisawa, “Benchmarking of state-of- the-art techniques,” Pattern Recognition, vol. 36, no 10, pp. 2271– 2285, Oct. 2003.

[9] M. Shi, Y. Fujisawa, T. Wakabayashi and F. Kimura, “Handwritten numeral recognition using gradient and curvature of gray scale image,” Pattern Recognition, vol. 35, no. 10, pp. 2051–2059, Oct 2002.

[10] LN. Teow and KF. Loe, “Robust vision-based features and classification schemes for off-line handwritten digit recognition,” Pattern Recognition, vol. 35, no. 11, pp. 2355–2364, Nov. 2002.

[11] K. Cheung, D. Yeung and RT. Chin, “A Bayesian framework for deformable pattern recognition with application to handwritten character recognition,” IEEE Trans PatternAnalMach Intell, vol. 20, no. 12, pp. 382– 1388, Dec. 1998.

[12] I.J. Tsang, IR. Tsang and DV Dyck, “Handwritten character recognition based on moment features derived from image partition,” in Int. Conf. image processing 1998, vol. 2, pp 939–942.

[13] H. Soltanzadeh and M. Rahmati, “Recognition of Persian handwritten digits using image profiles of multiple orientations,” Pattern Recognition Lett, vol. 25, no. 14, pp. 1569–1576, Oct.2004.

[14] FN. Said, RA. Yacoub and CY Suen, “Recognition of English and Arabic numerals using a dynamic number of hidden neurons” in Proc. 5th Int Conf. document analysis and recognition, 1999, pp 237–240

[15] J. Sadri, CY. Suen, and TD. Bui, “Application of support vector machines for recognition of handwritten Arabic/Persian digits,” in Proc. 2th Iranian Conf. machine vision and image processing, 2003, vol. 1,pp 300–307.
How to Cite
Indiran, H. P. (2020). Handwritten Character Recognition using Convolution Neural Networks in Python with Keras. Asian Journal For Convergence In Technology (AJCT), 5(3), 123-131. Retrieved from http://asianssr.org/index.php/ajct/article/view/946