Name
Abstract | ||
|---|---|---|
Car plate character recognition can be difficult since sample images might suffer perspective distortion, motion blur and poor lighting. Moreover, there are many character pattern variations such as size, font and color. Some related works are based on supervised approach and take a lot of training and labeled data to build an effective classification. Motivated by a graphu0027s capacity to model the underlying manifold and in the semi-supervised learning (SSL) ability to use few labeled data, this paper employs a graph-based SSL approach for character classification. We use four datasets: the first one was artificially generated by symbol rotation, blurring and contrast shifting. The second corresponds to Chars74K computer characters with font variations. The third and fourth are digits obtained from real car plates from the USA and Brazil. Classification experiments, based on artificial and real data, show that SSLu0027 s accuracy on a 10% labeled dataset is statistically comparable to approaches such as classical supervised algorithms k-NN, Naive Bayes, Decision Tree, Neural Networks and SVM. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BRACIS.2019.00132 | BRACIS |
Field | DocType | Citations |
Perspective distortion,Decision tree,Semi-supervised learning,Pattern recognition,Naive Bayes classifier,Computer science,Font,Support vector machine,Motion blur,Artificial intelligence,Artificial neural network | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| João Pedro Kerr Catunda | 1 | 0 | 0.34 |
| André Tavares da Silva | 2 | 0 | 0.34 |
| Lilian Berton | 3 | 16 | 7.82 |