In most image classification algorithms, the classifier model needs to train a large number of training samples. In practical application, labeling numbers of samples is a tedious and time-consuming task. So, how to select fewer suitable training samples from the numbers of unlabeled samples is a difficulty in the image classification algorithm. This paper proposes a trademark number recognition technique based on active learning algorithm. The method uses the human interaction to get trademark number area, and then uses the projection method to extract character characteristic which using the characteristic to split characters. Finally, use BvSB active learning algorithm to select high information samples which was used to train support vector machine classifier, and use the trained classifier to recognize trademark number. The experimental result shows that the classifier trained by the method has higher classification accuracy in the case of labeled fewer samples.
|Number of pages||14|
|Journal||Journal of Algorithms and Computational Technology|
|State||Published - Mar 1 2014|
- Active learning
- Support Vector Machine
- Trademark number recognition