https://github.com/athlan/polsl-digit-recognition
Robust vision-based features and classification schemes for offline handwritten digit recognition
https://github.com/athlan/polsl-digit-recognition
classification-schemes feature-extraction handwritten-digit-recognition matlab vision
Last synced: about 1 year ago
JSON representation
Robust vision-based features and classification schemes for offline handwritten digit recognition
- Host: GitHub
- URL: https://github.com/athlan/polsl-digit-recognition
- Owner: athlan
- License: mit
- Created: 2014-03-20T11:14:12.000Z (about 12 years ago)
- Default Branch: master
- Last Pushed: 2014-05-27T12:23:33.000Z (about 12 years ago)
- Last Synced: 2025-01-29T16:26:33.073Z (over 1 year ago)
- Topics: classification-schemes, feature-extraction, handwritten-digit-recognition, matlab, vision
- Language: Matlab
- Size: 11.4 MB
- Stars: 1
- Watchers: 6
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
##Implementation of hand written digit recognition in MatLab
Based on article:
* L.N. Teow, K.F. Loe, *Robust vision-based features and classification
schemes for off-line handwritten digit recognition*, Pattern Recogn. 35(2002)
----
###Abstract
We use well-established results in biological vision to construct a model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear discriminant system on these features, our model is relatively simple yet outperforms other models on the same data set. In particular, the best result is obtained by applying triowise linear support vector machines with soft voting on vision-based features extracted from deslanted images.
###Keywords
Handwritten digit recognition; Biological vision; Feature extraction; Linear discrimination; Multiclass classification