Text Localization in Real-world Images using Efficiently Pruned Exhaustive Search
An efficient method for text localization and recognition in real-world images is proposed. Thanks to effective pruning, it is able to exhaustively search the space of all character sequences in real time (200ms on a 640×480 image). The method exploits higher-order properties of text such as word text lines. We demonstrate that the grouping stage plays a key role in the text localization performance and that a robust and precise grouping stage is able to compensate errors of the character detector. The method includes a novel selector of Maximally Stable Extremal Regions (MSER) which exploits region topology. Experimental validation shows that 95.7% characters in the ICDAR dataset are detected using the novel selector of MSERs with a low sensitivity threshold. The proposed method was evaluated on the standard ICDAR 2003 dataset where it achieved state-of-the-art results in both text localization and recognition.
Lukás Neumann Jirí Matas
Centre for Machine Perception, Dept.Of Cybernetics Czech Technical University, Prague, Czech Republic
国际会议
北京
英文
687-691
2011-09-01（万方平台首次上网日期，不代表论文的发表时间）