Reconocimiento de caracteres mediante OCR (Optical Character Recognition)
DOI:
https://doi.org/10.30973/progmat/2018.10.1/6Palabras clave:
pixeles, matriz, iteración, normalizaciónResumen
En este trabajo, se implementaron técnicas para el reconocimiento digital de caracteres, utilizando la técnica OCR (Reconocimiento Óptico de Caracteres), que implementa métodos como binarización, etiquetado, esqueletización y proyección de trazas en las imágenes, para reconocer caracteres que optimizan los procesos en la abstracción y digitalización de libros, revistas u otras fuentes de información que pueden digitalizarse y luego manipularse en su formato digital.
Citas
Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y. A methodology for feature selection using multiobjective genetic algorithms for handwritten digit string recognition. International Journal of Pattern Recognition and Artificial Intelligence. 2003,17( 06), 903- 929. https://doi.org/10.1142/S021800140300271X
Bazzi, I., Schwartz, R., Makhoul, J. An omnifont open-vocabulary OCR system for English and Arabic. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1999, 21(6), 495-504. https://doi.org/10.1109/34.771314
Bortolozzi, F., Britto Jr, A.S., Oliveira, L.S., Morita M. Automatic recognition of handwritten numerical strings: A recognition and verification strategy. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002, 24(11), 1438-1454. https://doi.org/10.1109/TPAMI.2002.1046154
Ramírez-Ortegón, M.A., Tapia, E., Ramírez-Ramírez, L.L., Rojas,R., Cuevas, E. Transition pixel: A concept for binarization based on edge detection and gray-intensity histograms. Pattern Recognition. 2010, 43(4), 1233-1243. https://doi.org/10.1016/j.patcog.2009.11.006
Ramírez-Ortegón, M.A., Ramírez-Ramírez, L.L., Märgner,V., Messaoud, I.B., Cuevas, E., Rojas, R. An analysis of the transition proportion for binarization in handwritten historical documents. Proceedings of 8th International Conference on Document Analysis and Recognition. 2014, 17(2), 139-160. https://doi.org/10.1016/j.patcog.2014.02.003
Ramírez-Ortegón M.A., Märgner, Volker., Cuevas,E., Rojas, R. An optimization for binarization methods by removing binary artifacts. Pattern Recognition Letters. 2013, 34(11), 1299-1306. https://doi.org/10.1016/j.patrec.2013.04.007
Čisar, P., Čisar, S. M., Subošić, D., Đikanović, P., & Đukanović, S. Optimization Algorithms in Function of Binary Character Recognition. Acta Polytechnica Hungarica. 2015, 12(7), 77-87.
Yokobayashi, M., Wakahara,T. Binarization and Recognition of Degraded Characters Using a Maximum Separability Axis in Color Space and GAT Correlation. Pattern Recognition. 2006, 2, 885-888. https://doi.org/10.1109/ICPR.2006.326
Morita M., Sabourin, Robert., Bortolozzi, F., Suen, C.Y. Segmentation and recognition of handwritten dates: an HMM-MLP hybrid approach. Document Analysis and Recognition. 2003, 6(4), 248-262. https://doi.org/10.1007/s10032-003-0114-z
Yokobayashi, M., Wakahara,T. Segmentation and recognition of characters in scene images using selective binarization in color space and GAT correlation. Document Analysis and Recognition. 2005, 1,167-171. https://doi.org/10.1109/ICDAR.2005.208
Hoshen, J., R, Kopelman. Percolation and cluster distribution I: Cluster multiple labeling technique and critical concentration algorithm. Physical Review B. 1976 , 14(8), 3438-3445. https://doi.org/10.1103/PhysRevB.14.3438
Britto-Jr, A.S., Sabourin R., Bortolozzi F. The recognition of handwritten numeral strings using a two-stage HMM-based method. International Journal on Document Analysis and Recognition. 2003, 5(2-3), 2003. https://doi.org/10.1007/s10032-002-0085-5
Zhong, Y., Karu, K., & Jain, A. K. Locating text in complex color images. Pattern recognition. Pattern recognition. 1995, 28(10), 1523-1535. https://doi.org/10.1016/0031-3203(95)00030-4
Friston, K., Ashburner, J., Frith, C. D., Poline, J. B., Heather, J. D., & Frackowiak, R. S. Spatial registration and normalization of images. Human brain mapping. 2014, 3(3), 165-189. https://doi.org/10.1002/hbm.460030303
Mozaffari, S., Faez, K., Märgner, V., El-Abed, H. Lexicon reduction using dots for off-line Farsi/Arabic handwritten word recognition. Pattern Recognition Letters. 2008, 29(6), 724-734. https://doi.org/10.1016/j.patrec.2007.11.009
Lam, L., Lee, S. W., & Suen, C. Y. Hinning methodologies-a comprehensive survey. EEE Transactions on pattern analysis and machine intelligence. 2011, 14(9), 869-885. https://doi.ieeecomputersociety.org/10.1109/34.161346
Stentiford, F.W.M., & Mortimer, R.G. Some new heuristics for thinning binary handprinted characters for OCR. IEEE Transactions on Systems, Man, and Cybernetics. 1983, SMC-13(1), 81-84. https://doi.org/10.1109/TSMC.1983.6313034
Watrous, L. E., & Wheeler, Q. D. The out-group comparison method of character analysis. Systematic Biology. 1981, 30(1), 1-11. https://doi.org/10.1093/sysbio/30.1.1
Chen, J. L., & Lee, H. J. An efficient algorithm for form structure extraction using strip projection. Pattern recognition. 1998, 31(9), 1353-1368. https://doi.org/10.1016/S0031-3203(97)00156-8
Schneider, J. W., & Borlund, P. Matrix comparison, Part 1: Motivation and important issues for measuring the resemblance between proximity measures or ordination results. Journal of the Association for Information Science and Technology. 2007, 58(11), 1586-1595. https://doi.org/10.1002/asi.20643
S.F.J. Ceballos. MICROSOFT C#. LENGUAJE Y APLICACIONES. 2nd ed.: RA-MA EDITORIAL, 2007.
Elagouni, K., Garcia, C., Mamalet, F., & Sébillot, P. Text recognition in multimedia documents: a study of two neural-based ocrs using and avoiding character segmentation. Journal on Document Analysis and Recognition. 2014, 17(1), 19-31. https://doi.org/10.1007/s10032-013-0202-7
Li, H., Doermann, D., & Kia, O. Automatic text detection and tracking in digital video. IEEE transactions on image processing. 2000, 9(1), 147-156. https://doi.org/10.1109/83.817607
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