A wavelet-based descriptor for handwritten numeral classification
In this work we propose descriptors for handwritten digit recognition based on multiresolution features by using the CDF 9/7 Wavelet Transform and Principal Component Analysis, in order to improve the classification performance and obtain a strong reduction on the size of the digit representation. T...
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| Formato: | Acta de conferencia Capítulo de libro |
| Lenguaje: | Inglés |
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2012
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
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| LEADER | 08822caa a22008897a 4500 | ||
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| 001 | PAPER-9103 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518203858.0 | ||
| 008 | 190411s2012 xx ||||fo|||| 10| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-84874274467 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Seijas, L.M. | |
| 245 | 1 | 2 | |a A wavelet-based descriptor for handwritten numeral classification |
| 260 | |c 2012 | ||
| 270 | 1 | 0 | |m Seijas, L.M.; Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina; email: lseijas@dc.uba.ar |
| 506 | |2 openaire |e Política editorial | ||
| 504 | |a Nicholl, P., Amira, A., Bouchaffra, D., Perrott, R., A statistical multirresolution approach for face recognition using structural hidden markov models (2008) EURASIP Journal on Advances in Signal Processing, 2008, pp. 1-13 | ||
| 504 | |a Kaplan, L., Murenzi, R., Pose estimation of sar imagery using the two dimensional continuous wavelet transform (2003) Pattern Recognition Letters, 24, pp. 2269-2280 | ||
| 504 | |a Chen, C., Chen, C., Chen, C., A comparison of texture features based on svm and som (2006) 18th International Conference on Pattern Recognition. Hong Kong: ICPR 2006, pp. 630-633 | ||
| 504 | |a Wunsch, P., Laine, A., Wavelet descriptors for multiresolution recognition of handprinted characters (1995) Pattern Recognition, 28 (8), pp. 1237-1249 | ||
| 504 | |a Chen, G.Y., Bui, T.D., Krzyzak, A., Contour-based handwritten numeral recognition using multiwavelets and neural networks (2003) Pattern Recognition, 36 (7), pp. 1597-1604 | ||
| 504 | |a Romero, D., Ruedin, A., Seijas, L., Wavelet based feature extraction for handwritten numerals (2009) Image Analysis and Processing (ICIAP 2009), LNCS, 5716, pp. 374-383. , Springer | ||
| 504 | |a Correia, S., De Carvalho, J., Optimizing the recognition rates of unconstrained handwritten numerals using biorthogonal spline wavelets (2000) Pattern Recognition, 2000. Proceedings. 15th International Conference On. Barcelona, Spain: ICPR 2000, pp. 251-254 | ||
| 504 | |a Seijas, L., Segura, E., Detection of ambiguous patterns using svms: Application to handwritten numeral recognition (2009) Computer Analysis of Images and Patterns (CAIP 2009) - Lecture Notes in Computer Science, LNCS, 5702, pp. 840-847 | ||
| 504 | |a Shukla, P., (2003) Complex Wavelet Transforms and Their Applications, , M.Phil. Thesis. Glasgow (United Kingdom): Dept.of Electronic and Electrical Engineering, University of Strathclyde | ||
| 504 | |a Mallat, S., (1999) A Wavelet Tour of Signal Processing, , Academic Press | ||
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| 504 | |a Vapnik, V., (1995) The Nature of Statistical Learning Theory, , Springer Verlag, New York | ||
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| 504 | |a Oliveira, L., Sabourin, R., Support vector machines for handwritten numerical string recognition (2004) 9th IEEE International Workshop on Frontiers in Handwritten Recognition, pp. 39-44. , Washington DC: IEEE Computer Society | ||
| 504 | |a Haykin, S., (1999) Neural Networks A Comprehensive Foundation, , Prentice Hall | ||
| 504 | |a Liu, C., Nakashima, K., Sako, H., Fujisawa, H., Handwritten digit recognition: Benchmarking of state-of-the-art techniques (2003) Pattern Recognition, 36, pp. 2271-2285 | ||
| 504 | |a Gorgevik, D., Cakmakov, D., An efficient three-stage classifier for handwritten digit recognition (2004) 17th International Conference on Pattern Recognition, 4, pp. 507-510. , IEEE Computer Society | ||
| 504 | |a Seijas, L., Segura, E., Detection of ambiguous patterns in a som based recognition system: Application to handwritten numeral classification (2007) 6th International Workshop on Self-Organizing Maps, , Germany: Bielefeld University | ||
| 504 | |a Lee, S., Off-line recognition of totally unconstrained handwritten numerals using multilayer cluster neural network (1996) IEEE Transactions on Pattern Analysis and Machine Intelligence, 18 (6), pp. 648-652 | ||
| 504 | |a Kegl, B., Busa-Fekete, R., Boosting products of base classifiers (2009) Proceedings of the 26th International Conference on Machine Learning, , Montreal, Canada | ||
| 504 | |a Lauer, F., Suen, C., Bloch, G., A trainable feature extractor for handwritten digit recognition (2007) Pattern Recognition, 40, pp. 1816-1824 | ||
| 504 | |a Suen, C., Kiu, K., Strathy, N., Sorting and recognizing cheques and financial documents (1999) Document Analysis Systems: Theory and Practice, S.W.Lee and Y.Nakano (Eds.), LNCS, 1655, pp. 173-187. , Springer | ||
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| 504 | |a Ciresan, D., Meier, U., Gambardella, L., Schmidhuber, J., Handwritten digit recognition with a committee of deep neural nets on gpus (2011) Technical Report No. IDSIA-03-11. IDSIA / USI-SUPSI, Dalle Molle Institute for Artificial Intelligence, , SwitzerlandA4 - Presidenza del Consiglio dei Ministri; Governo Italiano; Ministero dello Sviluppo Economico; IAPR; Rete Puglia | ||
| 520 | 3 | |a In this work we propose descriptors for handwritten digit recognition based on multiresolution features by using the CDF 9/7 Wavelet Transform and Principal Component Analysis, in order to improve the classification performance and obtain a strong reduction on the size of the digit representation. This allows for a higher precision in the recognizers and, at the same time, lower training costs, especially for large datasets. Experiments were carried out with the CENPARMI and MNIST databases, widely used in the literature for this kind of problems, combining classifiers of the Support Vector Machine type. The recognition rates are good, comparable to those reported in previous works. © 2012 IEEE. |l eng | |
| 593 | |a Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina | ||
| 690 | 1 | 0 | |a DESCRIPTOR |
| 690 | 1 | 0 | |a DIGIT RECOGNITION |
| 690 | 1 | 0 | |a DIMENSION REDUCTION |
| 690 | 1 | 0 | |a MULTIRESOLUTION FEATURES |
| 690 | 1 | 0 | |a SUPPORT VECTOR MACHINES |
| 690 | 1 | 0 | |a CLASSIFICATION PERFORMANCE |
| 690 | 1 | 0 | |a COMBINING CLASSIFIERS |
| 690 | 1 | 0 | |a DESCRIPTORS |
| 690 | 1 | 0 | |a DIGIT RECOGNITION |
| 690 | 1 | 0 | |a DIGIT REPRESENTATION |
| 690 | 1 | 0 | |a DIMENSION REDUCTION |
| 690 | 1 | 0 | |a HANDWRITTEN DIGIT RECOGNITION |
| 690 | 1 | 0 | |a HANDWRITTEN NUMERAL |
| 690 | 1 | 0 | |a LARGE DATASETS |
| 690 | 1 | 0 | |a MNIST DATABASE |
| 690 | 1 | 0 | |a MULTI-RESOLUTION FEATURE |
| 690 | 1 | 0 | |a RECOGNITION RATES |
| 690 | 1 | 0 | |a TRAINING COSTS |
| 690 | 1 | 0 | |a CHARACTER RECOGNITION |
| 690 | 1 | 0 | |a CLASSIFICATION (OF INFORMATION) |
| 690 | 1 | 0 | |a PRINCIPAL COMPONENT ANALYSIS |
| 690 | 1 | 0 | |a SUPPORT VECTOR MACHINES |
| 700 | 1 | |a Segura, E.C. | |
| 711 | 2 | |c Bari |d 18 September 2012 through 20 September 2012 |g Código de la conferencia: 95717 | |
| 773 | 0 | |d 2012 |h pp. 653-658 |p Proc. Int. Workshop Front. Handwriting Recogn. IWFHR |n Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR |x 15505235 |z 9780769547749 |t 13th International Conference on Frontiers in Handwriting Recognition, ICFHR 2012 | |
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| 856 | 4 | 0 | |u https://doi.org/10.1109/ICFHR.2012.174 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_15505235_v_n_p653_Seijas |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15505235_v_n_p653_Seijas |y Registro en la Biblioteca Digital |
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