Handwritten Digit Recognition Based-on Bernoulli Mixtures

Zhen Hua Zhao; Xiao Hong Hao

doi:10.4028/www.scientific.net/AMR.317-319.901

Paper Titles

Lane Markers Detection Based on Consecutive Threshold Segmentation
p.881

Study on a Method for Measuring the Attitude of Protable Three-Coordinate Equipment Based on Monocular Vision
p.886

Research on Underwater Target Tracking Based on Contour Detection
p.890

A Novel Illumination Compensation Method Using Wavelet Packet Transformation
p.897

Handwritten Digit Recognition Based-on Bernoulli Mixtures
p.901

Fixed Viewpoint Imaging and Correction Model of Liquid Column Manometer
p.905

Applications of Visible and near-Infrared Hyperspectral Imaging for Non-Destructive Detection of the Agricultural Products
p.909

The Finite Ridgelet Transform for Defeat Detection of Quill
p.915

The Application of Fast Moving Target Detection Technology in Hanger Intelligent Surveillance System
p.919

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 317-319Handwritten Digit Recognition Based-on Bernoulli...

Handwritten Digit Recognition Based-on Bernoulli Mixtures

Abstract:

A method based on Bernoulli mixtures is presented in this paper for the purpose of describing the pattern of probability distribution for each of the data sets of handwritten numerals (0-9), and then classifiers are formed from these. The test samples are recognized by their posterior probabilities conditioned on every classifier. Experimental results show that our method is superior to conventional methods on robustness and accuracy.

You might also be interested in these eBooks

Equipment Manufacturing Technology and Automation

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 317-319)

Pages:

901-904

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.317-319.901

Citation:

Cite this paper

Online since:

August 2011

Authors:

Zhen Hua Zhao, Xiao Hong Hao

Keywords:

Bernoulli Mixtures, EM Algorithm, Likelihood Function, Probability Density

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Y. Al-Ohali, M. Cheriet, and C. Suen, "Databases for recognition of handwritten Arabic cheques," Pattern Recognition, 36:111–121, 2003.

DOI: 10.1016/s0031-3203(02)00064-x

Google Scholar

[2] A. Juan and E. Vidal, "On the use of Bernoulli mixture models for text classification," Pattern Recognition, 35(12):2705– 2710, Dec. 2002.

DOI: 10.1016/s0031-3203(01)00242-4

Google Scholar

[3] Hu. J and Yan. H, " Structural primitive extraction and coding for handwritten numeral recognition ," Pattern Recognition, 1998, 31 (5): 493-509.

DOI: 10.1016/s0031-3203(97)00095-2

Google Scholar

[4] G. Henkelman, G.Johannesson and H. Jónsson, in: Theoretical Methods in Condencsed Phase Chemistry, edited by S.D. Schwartz, volume 5 of Progress in Theoretical Chemistry and Physics, chapter, 10, Kluwer Academic Publishers (2000).

Google Scholar

[5] R. O. Duda and P. E. Hart, "Pattern Classification and Scene Analysis", Wiley, (1973)

Google Scholar

[6] J. Dong, A. Krzyzak, "Local learning framework for handwritten character recognition", Engineering Applications of Artificial Intelligence, 2002 , 15 (2) , p.151~159

DOI: 10.1016/s0952-1976(02)00024-6

Google Scholar

[7] Bailing Zhang, Minyue Fu , Hong Yan , "Handwritten digit recognition by adaptive-subspace self-organizing map" IEEE Trans1 Neural Network , 1999 , 10(4) : 589~603

DOI: 10.1109/72.774267

Google Scholar

[8] C.-L. Liu, K. Nakashima, H. Sako, and H. Fujisawa, "Handwritten Digit Recognition: Benchmarking of State-of-the-Art Techniques," Pattern Recognition, vol. 36, no. 10, pp.2271-2285, 2009.

DOI: 10.1016/s0031-3203(03)00085-2

Google Scholar