Name
Abstract | ||
|---|---|---|
This paper focuses on the importance of global features for online character recognition. Global features represent the relationship between two temporally distant points in a handwriting pattern. For example, it can be defined as the relative vector of two xy-coordinate features of two temporally separated points. Most existing online character recognition methods do not utilize global features, since their non-Markovian property prevents the use of the traditional recognition methodologies, such as dynamic time warping and hidden Markov models. However, we can understand the importance of, for example, the relationship between the starting and the ending points by attempting to discriminate ''0'' and ''6''. This relationship cannot be represented by local features defined at individual points but by global features. Since O(N^2) global features can be extracted from a handwriting pattern with N points, selecting those that are truly discriminative is very important. In this paper, AdaBoost is employed for feature selection. Experiments prove that many global features are discriminative and the combined use of local and global features can improve the recognition accuracy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.patrec.2013.03.036 | Pattern Recognition Letters |
Keywords | Field | DocType |
temporally distant point,global feature,recognition accuracy,temporally separated point,online character recognition,handwriting pattern,traditional recognition methodology,combined use,existing online character recognition,local feature,feature selection,feature extraction | Computer vision,AdaBoost,Pattern recognition,Handwriting,Feature selection,Dynamic time warping,Feature extraction,Feature (machine learning),Artificial intelligence,Hidden Markov model,Discriminative model,Mathematics | Journal |
Volume | ISSN | Citations |
35, | 0167-8655 | 6 |
PageRank | References | Authors |
0.46 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| minoru mori | 1 | 52 | 7.49 |
| Seiichi Uchida | 2 | 790 | 105.59 |
| Hitoshi Sakano | 3 | 126 | 13.02 |