Abstract
In this paper, we propose a novel process to optical character recognition (OCR) used in real environments, such as gas-meters and electricity-meters, where the quantity of noise is sometimes as large as the quantity of good signal. Our method combines two algorithms an artificial neural network on one hand, and the k-nearest neighbor as the confirmation algorithm. Our approach, unlike other OCR systems, it is based on the angles of the digits rather than on pixels. Some of the advantages of the proposed system are: insensitivity to the possible rotations of the digits, the possibility to work in different light and exposure conditions, the ability to deduct and use heuristics for character recognition. The experimental results point out that our method with moderate level of training epochs can produce a high accuracy of 99.3 % in recognizing the digits, proving that our system is very successful.
Similar content being viewed by others
References
Atukorale AS, Suganthan PN, Downs T (2000) On the performance of the HONG network for pattern classification. In: Proceedings of the IEEE-INNS-ENNS international joint conference on neural networks, vol 2, pp 285–290
Blue JL, Candela GT, Grother PJ, Chellappa R, Wilson CL (2004) Evaluation of pattern classifiers for fingerprint and ocr applications. Pattern Recognit 27(4):485–501
Boiman O, Shechtman E, Irani M (2008) In defense of nearest-neighbor based image classification. In: IEEE conference on computer vision and pattern recognition, pp 1–8
Le Cun Y, Boser B, Denker JS, Howard RE, Habbard W, Jackel LD, Henderson D (1990) Handwritten digit recognition with a back-propagation network. Adv Neural Inf Process Syst 2:396–404
Garcez ASA, Zaverucha G (1999) The connectionist inductive learning and logic programming system. Appl Intell 11(1):59–77
Garris MD, Wilkinson RA (1992) Handwritten segmented characters database. Technical report special database NIST 3, February 1992
Guyon I, Haralick MR, Hull JJ, Phillips TT (1997) Data sets for OCR and document image understanding research. In: Handbook of character recognition and document image analysis, pp 779–799
Ha TM, Bunke H (1997) Off-line, handwritten numeral recognition by perturbation method. IEEE Trans Pattern Anal Mach Intell 19(5):535–539
Hoehfeld M, Fahlman SE (1995) Learning with limited numerical precision using the cascade-correlation algorithm. IEEE Trans Neural Netw 3(4):602–611
Huang C-C, Lee H-M (2004) A grey-based nearest neighbor approach for missing attribute value prediction. Appl Intell 3(20):239–252
Jain AK, Zongker D (1997) Representation and recognition of handwritten digits using deformable templates. IEEE Trans Pattern Anal Mach Intell 19(12):1386–1390
Le Cun Y, Jackel L, Bottou L, Brunot A, Cortes C, Denker J, Drucker H, Guyon I, Muller U, Sackinger E, Simard P, Vapnik V (2004) Comparison of learning algorithms for handwritten digit recognition. In: Proceedings of the international conference on artificial neural networks, pp 53–60
Lee Y (1991) Handwritten digit recognition using k nearest-neighbor, radial-basis function, and back-propagation neural networks. Neural Comput 3(3):440–449
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133
Mori S, Suen CY, Yamamoto K (1992) Historical review of ocr research and development. In: Proceedings of IEEE, special issue on OCR, pp 1029–1075
Rabiner LR, Wilpon JG, Soong FK (1999) High performance connected digit recognition using hidden Markov models. IEEE Trans Acoust Speech Signal Process 37(8):1214–1225
Russ JC (2011) The image processing handbook. CRC Press, Boca Raton
Schaal S, Atkeson CG, Vijayakumar S (2002) Scalable techniques from nonparametric statistics for real time robot learning. Appl Intell 17(1):49–60
Shapiro LG, Stockman GC (2002) Computer vision. Prentice Hall, New Jersey
Acknowledgements
This work was supported by a grant of the Romanian National Authority for Scientific Research, CNCS-UEFISCDI, project number PN-II-RU-TE-2011-3-0113.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Matei, O., Pop, P.C. & Vălean, H. Optical character recognition in real environments using neural networks and k-nearest neighbor. Appl Intell 39, 739–748 (2013). https://doi.org/10.1007/s10489-013-0456-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-013-0456-2