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 640x480 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.

[1]  Rainer Lienhart,et al.  Localizing and segmenting text in images and videos , 2002, IEEE Trans. Circuits Syst. Video Technol..

[2]  S.M. Lucas,et al.  ICDAR 2005 text locating competition results , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[3]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[4]  Jing Zhang,et al.  Character Energy and Link Energy-Based Text Extraction in Scene Images , 2010, ACCV.

[5]  Jiri Matas,et al.  A New Class of Learnable Detectors for Categorisation , 2005, SCIA.

[6]  Simon M. Lucas,et al.  ICDAR 2003 robust reading competitions , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[7]  Torsten Caesar,et al.  Estimating the baseline for written material , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[8]  Yonatan Wexler,et al.  Detecting text in natural scenes with stroke width transform , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Xiaofan Lin,et al.  Reliable OCR solution for digital content re-mastering , 2001, IS&T/SPIE Electronic Imaging.

[10]  Silke Wagner,et al.  Using web search engines to improve text recognition , 2008, 2008 19th International Conference on Pattern Recognition.

[11]  Jiri Matas,et al.  Estimating hidden parameters for text localization and recognition , 2011 .

[12]  Cheng-Lin Liu,et al.  Text Localization in Natural Scene Images Based on Conditional Random Field , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[13]  Alan L. Yuille,et al.  Detecting and reading text in natural scenes , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[14]  Jiri Matas,et al.  A Method for Text Localization and Recognition in Real-World Images , 2010, ACCV.