Natural Scene Text Detection Based on Multi-Channel FASText
PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON AUTOMATIC CONTROL AND INFORMATION ENGINEERING (ICACIE 2017)(2017)
摘要
In view of the complexity of background and the variety of text in natural scene images, a multi-channel FASText based text detection method for natural scene images is proposed. To detect more texts as much as possible, the character candidates are extracted by proposed multi-channel FASText algorithm from the R, G and B component image respectively. Then, texture features of the character candidates are extracted to train a random forest character classifier and the non-characters are eliminated. At last, the character regions are merged into text regions according to the color distance feature and geometric adjacency feature. The proposed approach on ICDAR 2013 dataset achieves 76.76%, 80.17%, and 78.43% in recall rate, precision rate and f-score respectively. Compared to other state-of-the-art methods, both the recall rate and f-score are improved. Experimental result shows that the proposed method is effective to natural scene text detection.
更多查看译文
关键词
multi-channel,FASText,natural scene,text detection
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络