Predspracovanie obrazu pre optické rozpoznávanie ... - TUKE

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FEI TU v Košiciach Diplomová práca List č. 90 resolution of 200 dpi 3 . The experiments showed that the best size of input layer of neural network is16 ×16 neurons with this this scanned image in this scanning resolution. Also the size of hidden layer was determined and the experiment showed that the hidden layer should have at least 10 neurons. There were done some experiments with the training pattern generating. If the image preprocessing has to be good enough, the training pattern should have at least 8000 samples and their distribution should be controled. The best recognition success that was achieved after image preprocessing was 87.9% of letters correctly recognized. The software used for OCR was ABBYY FineReader 7.0 (trial version). Contribution to the research domain This master’s thesis does a research of image preprocessing before optical character recognition using neural networks. It is a benefit to the research domain because the image preprocessing using neural networks isn’t ade- quately examined and it has a lot of potential. I think that the nonlinear adaptive filtering which can be done using neural networks is much more better than linear filtering and it is the future of the image processing and preprocessing. You can find some theoretical and practical basics in this master’s thesis about this topic. Conclusion As this master’s thesis shows, the neural networks can be used for image preprocessing pretty well with many advantages over normal image preprocessing methods. For more information about mentioned experiments and application please read the full master’s thesis (in slovak language). 3 dots per inch
FEI TU v Košiciach Diplomová práca List č. 91 References [1] P. Sinčák, G. Andrejková: Neurónové siete - inžiniersky prístup (dopredné ne- urónové siete) č.1., ELFA Press, 1996 [2] P. Sinčák, G. Andrejková: Neurónové siete - inžiniersky prístup (rekurentné a modulárne neurónové siete) č.2., ELFA Press, 1996 [3] R. Jakša: Neuronriadenie: využitie neurónových sietí v inteligentnom riadení, Dizertačná práca, Technická univerzita Košice, 1999 [4] M. Užák: Vizualizácia a interakcia v procese učenia neurónových sietí, Dip- lomová práca, Technická univerzita Košice, 2005 [5] Trevor Clarkson: Applications of Neural Networks in Telecommunications, Strand, London WC2R 2LS, UK, 2000 [6] D. de Ridder, R. P.W. Duin, M. Egmont-Petersen, L. J. van Vliet, P. W. Ver- beek: Nonlinear image processing using artificial neural networks, 2003 [7] R. Chellappa, K. Fukushima, A. K. Katsaggelos, S.Y. Kung, Y. Lecun, N. M. Nasrabadi, T. A. Poggio: Applications of Artificial Neural Networks to Image Processing, IEEE transactions on image processing, vol. 7, no. 8, august 1998 [8] S.Y. Kung, J. N. Hwang: Neural Networks for Intelligent Multimedia Proces- sing, Proceedings of the IEEE, vol. 86, no. 6, june 1998 [9] Optical character recognition http://en.wikipedia.org/wiki/Optical_character_recognition [10] Mustek, Inc.: Understanding OCR http://www2.mustek.com/Class/ocrinfo.html [11] Eric W. Brown: Character Recognition by Feature Point Extraction http://www.ccs.neu.edu/home/feneric/charrec.html, 1992
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Poďakovanie Chcem poďakovať môj
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