Abstract
The article presents an original approach to optical character recognition (OCR) used in real environments, such as gas- and electricity-meters, where the quantity of noise is sometimes as large as the quantity of good signal. This approach uses two algorithms for better results. These are a neural network on one hand, respectively the k-nearest neighbor as the confirmation algorithm. Unlike other OCR systems, this one is based on the angles of the digits, rather than on pixels. This makes it insensitive to the possible rotations of the digits, respectively to the quantity of noise that may appear in an image. We will prove that the approach has several advantages, such as: 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.
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References
Abdelazeem, S.: Comparing arabic and latin handwritten digits recognition problems. World Academy of Science, Engineering and Technology 54 (2009)
Alpaydin, E.: Combined 5 x 2 cv f test for comparing supervised classification learning algorithms. Neural Computation (11) (1999)
Bay, S.D.: Nearest neighbor classification from multiple feature subsets. Intelligent Data Analysis (3) (1999)
Blue, J.L., Candela, G.T., Grother, P.J., Chellappa, R., Wilson, C.L.: Evaluation of pattern classifiers for fingerprint and ocr applications. Pattern Recognition 27(4) (2004)
Le Cun, Y., Boser, B., Denker, J.S., Howard, R.E., Habbard, W., Jackel, L.D., Henderson, D.: Handwritten digit recognition with a back-propagation network. pp. 396–404 (1990)
Gu, X., Yub, D., Zhanga, L.: Image thinning using pulse coupled neural network. Pattern Recognition Letters 25(9) (2004)
Hoehfeld, M., Fahlman, S.E.: Learning with limited numerical precision using the cascade-correlation algorithm. IEEE Transactions on Neural Networks 3(4), 602–611 (1995)
Impedovo, S., Ottaviano, L., Occhinegro, S.: Optical character recognition–a survey. Internationl Journal of Pattern Recognition 5(1-2) (May 1991)
Jain, A.K., Zongker, D.: Representation and recognition of handwritten digits using deformable templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(12) (1997)
LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Muller, U., Säckinger, E., Simard, P., Vapnik, V.: Comparison of learning algorithms for handwritten digit recognition. In: International Conference on Artificial Neural Networks, pp. 53–60 (2004)
Lee, Y.: Handwritten digit recognition using k nearest-neighbor, radial-basis function, and backpropagation neural networks. Neural Computation 3(3), 440–449 (1991)
Mori, S., Suen, C.Y., Yamamoto, K.: Historical review of ocr research and development. Pattern Recognition 80(7) (2002)
Rabiner, L.R., Wilpon, J.G., Soong, F.K.: High performance connected digit recognition using hidden markov models. IEEE Transactions on Acoustics, Speech and Signal Processing 37(8) (1999)
Russ, A.: The Image Processing Handbook. CRC (2002)
Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice Hall (2002)
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Matei, O., Pop, P.C., Vălean, H. (2012). A Robust Approach to Digit Recognition in Noisy Environments. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_62
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DOI: https://doi.org/10.1007/978-3-642-31087-4_62
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