On Combining Multiple Segmentations in Scene Text Recognition

An end-to-end real-time scene text localization and recognition method is presented. The three main novel features are: (i) keeping multiple segmentations of each character until the very last stage of the processing when the context of each character in a text line is known, (ii) an efficient algorithm for selection of character segmentations minimizing a global criterion, and (iii) showing that, despite using theoretically scale-invariant methods, operating on a coarse Gaussian scale space pyramid yields improved results as many typographical artifacts are eliminated. The method runs in real time and achieves state-of-the-art text localization results on the ICDAR 2011 Robust Reading dataset. Results are also reported for end-to-end text recognition on the ICDAR 2011 dataset.

[1]  Christof Koch,et al.  AdaBoost for Text Detection in Natural Scene , 2011, 2011 International Conference on Document Analysis and Recognition.

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

[3]  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.

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

[5]  C. V. Jawahar,et al.  Top-down and bottom-up cues for scene text recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Jean-Michel Jolion,et al.  Object count/area graphs for the evaluation of object detection and segmentation algorithms , 2006, International Journal of Document Analysis and Recognition (IJDAR).

[7]  Jiřı́ Matas,et al.  Real-time scene text localization and recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Zhuowen Tu,et al.  Detecting Texts of Arbitrary Orientations in 1 Natural Images , 2012 .

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

[10]  Chunheng Wang,et al.  Scene text detection using graph model built upon maximally stable extremal regions , 2013, Pattern Recognit. Lett..

[11]  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..

[12]  Andreas Dengel,et al.  ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images , 2011, 2011 International Conference on Document Analysis and Recognition.

[13]  Chucai Yi,et al.  Text String Detection From Natural Scenes by Structure-Based Partition and Grouping , 2011, IEEE Transactions on Image Processing.

[14]  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.

[15]  Lionel Prevost,et al.  2009 10th International Conference on Document Analysis and Recognition Text Detection and Localization in Complex Scene Images using Constrained AdaBoost Algorithm , 2022 .

[16]  Jiri Matas,et al.  Text Localization in Real-World Images Using Efficiently Pruned Exhaustive Search , 2011, 2011 International Conference on Document Analysis and Recognition.

[17]  Kai Wang,et al.  End-to-end scene text recognition , 2011, 2011 International Conference on Computer Vision.

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

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