Scene text detection using adaptive color reduction, adjacent character model and hybrid verification strategy
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- 作者：Hui Wu ; Beiji Zou ; Yu-qian Zhao ; Jianjing Guo
- 关键词：Scene text detection ; Adaptive color reduction ; Adjacent character model ; Hybrid text verification strategy
- 刊名：The Visual Computer
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：33
- 期：1
- 页码：113-126
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision;
- 出版者：Springer Berlin Heidelberg
- ISSN：1432-2315
- 卷排序：33
文摘
Text detection is a primary task for text recognition and understanding, which can be used in many image analysis techniques. In this paper, we propose an effective scene text detection method including three major steps: connected components (CCs) extraction, character-linking and text/non-text classification. First, for CCs extraction, we design an adaptive color reduction scheme by analyzing image color histogram, which reasonably selects color centers and generates unfixed number of color layers for images in different color complexities. Then, for character-linking, an adjacent character model is built by training an extreme learning machine (ELM), instead of setting various thresholds in previous approaches. Finally, a hybrid text verification strategy is adopted, combining convolutional neural network with ELM for text/non-text classification and performing better than just using one of them. Experimental results on some publicly available datasets illustrate the effectiveness of our method and comparative results with some state-of-the-art algorithms demonstrate our competitiveness.