Name
Abstract | ||
|---|---|---|
There have been many proposals to extend the basic two-class SVM classifier for multiclass classification and it is established that among these extensions binary-structured hierarchical SVMs is the most efficient computationally. However, determining an effective binary structure by recursively dividing the classes is a major research issue. We describe a new classifier, GP-SVM, based on greedy partitioning of classes and demonstrate that GP-SVM gives better classification accuracy than all major combinational techniques besides having the computational advantages. The advantages of GP-SVM is better realized when the number of classes is large. We demonstrate this advantage in recognition of printed Odia character. We built a corpus of 10025 tagged Odia aksharas collected over multiple printed documents of different fonts. We used a very modest number of features. GP-SVM with 133 classes yielded 95% accuracy of recognition. During the learning process of GP-SVM, the proposed system could learn the taxonomy of character-shapes of Odia script. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/NCVPRIPG.2015.7490018 | 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) |
Keywords | Field | DocType |
greedy partitioning based tree structured multiclass SVM,Odia OCR,two-class SVM classifier,multiclass classification,GP-SVM,classification accuracy,combinational techniques,printed Odia character,Odia aksharas,multiple printed documents,learning process,character-shapes,Odia script | Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Svm classifier,Classifier (linguistics),Machine learning,Recursion,Binary number,Multiclass classification | Conference |
ISSN | Citations | PageRank |
2372-658X | 0 | 0.34 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandeep Kumar Sahu | 1 | 19 | 2.63 |
| Arun K. Pujari | 2 | 420 | 48.20 |
| Vikas Kumar 0003 | 3 | 25 | 4.76 |
| Venkateswara Rao Kagita | 4 | 59 | 8.13 |
| Vineet Padmanabhan | 5 | 216 | 25.90 |