A New Method for Text Verification Based on Random Forests
Yang Zhang, Chunheng Wang, Baihua Xiao, Cunzhao Shi · 2012
Text in image or video frames contains a lot of high-level semantics which can be useful for multimedia indexing, management. Coarse text detection results may contain many false alarms, which makes it necessary to eliminate the false alarms for further recognition. As text has distinct textural features, texture-based classifier such as SVM, MLP and Adaboost has been used to classify the detection regions as text or non-text region. In this paper, a random forests based method for text verification is proposed. The reason of choosing random forests lies in: 1) its ability of maintaining accuracy in small labeled dataset and 2) its good performance in unbalanced dataset as in the case of unbalanced text and non-text distribution. Furthermore, we propose to merge different random forests trained with different kinds of features to improve the accuracy of classification. The comprehensive experimental results show that our methods are effective.