Proceedings ArticleDOI
Handwritten Digit Recognition by Combining SVM Classifiers
D. Gorgevik,Dusan Cakmakov +1 more
- Vol. 2, pp 1393-1396
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TLDR
A cooperation of four SVM classifiers for handwritten digit recognition, each using different feature set is examined, and it is shown that it is difficult to exceed the recognition rate of a single, well-tuned SVMclassifier applied straightforwardly on all feature sets.Abstract:
Recent results in pattern recognition have shown that SVM (support vector machine) classifiers often have superior recognition rates in comparison to other classification methods In this paper, a cooperation of four SVM classifiers for handwritten digit recognition, each using different feature set is examined We investigate the advantages and weaknesses of various cooperation schemes based on classifier decision fusion using statistical reasoning The obtained results show that it is difficult to exceed the recognition rate of a single, well-tuned SVM classifier applied straightforwardly on all feature sets In our experiments only one of the cooperation schemes exceeds the recognition rate of a single SVM classifier However, the classifier cooperation reduces the classifier complexity and need for training samples, decreases classifier training time and sometimes improves the classifier performanceread more
Citations
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Journal ArticleDOI
ARDIS: a Swedish historical handwritten digit dataset
TL;DR: Experimental results show that machine learning algorithms, including deep learning methods, provide low recognition accuracy as they face difficulties when trained on existing datasets and tested on ARDIS dataset, which proves that AR DIS dataset has unique characteristics.
Journal ArticleDOI
A Study of Moment Based Features on Handwritten Digit Recognition
TL;DR: This paper presents a script invariant handwritten digit recognition system for identifying digits written in five popular scripts of Indian subcontinent, namely, Indo-Arabic, Bangla, Devanagari, Roman, and Telugu and observes that Multilayer Perceptron MLP classifier outperforms the others.
Proceedings ArticleDOI
Improving Classification of an Industrial Document Image Database by Combining Visual and Textual Features
TL;DR: A new method for classifying document images by combining textual features extracted with the Bag of Words (BoW) technique and visual features extracting with the BoVW technique, which significantly improves the classification performances.
Proceedings ArticleDOI
Support Vector Machine based automatic electric meter reading system
TL;DR: The traditional method of manual electric meter reading is very tedious and is prone to lot of errors and has a lot of disadvantages, so a better option is to fit a image acquisition device like camera in front of the Meter that will take realtime pictures of the meter readings.
Proceedings ArticleDOI
Handwritten digits recognition base on improved LeNet5
TL;DR: An improved Le net5 is presented by replacing the last two layers of the LeNet5 structure with Support Vector Machines (SVM) classifier, which can outperform both SVMs and LeNet 5 in handwritten digits recognition.
References
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Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Journal ArticleDOI
On combining classifiers
TL;DR: A common theoretical framework for combining classifiers which use distinct pattern representations is developed and it is shown that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision.
Journal ArticleDOI
Methods of combining multiple classifiers and their applications to handwriting recognition
TL;DR: On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly.