A Machine Learning Approach to Hypothesis Decoding in Scene Text Recognition

Scene Text Recognition (STR) is a task of localizing and transcribing textual information captured in real-word images. With its increasing accuracy, it becomes a new source of textual data for standard Natural Language Processing tasks and poses new problems because of the specific nature of Scene Text. In this paper, we learn a string hypotheses decoding procedure in an STR pipeline using structured prediction methods that proved to be useful in automatic Speech Recognition and Machine Translation. The model allow to employ a wide range of typographical and language features into the decoding process. The proposed method is evaluated on a standard dataset and improves both character and word recognition performance over the baseline.

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

[2]  Chunheng Wang,et al.  Scene Text Recognition Using Part-Based Tree-Structured Character Detection , 2013, CVPR 2013.

[3]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[4]  Jeff A. Bilmes.  Graphical models and automatic speech recognition , 2002 .

[5]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.

[6]  John Langford,et al.  Search-based structured prediction , 2009, Machine Learning.

[7]  Jun Guo,et al.  Text extraction from natural scene image: A survey , 2013, Neurocomputing.

[8]  Hartmut Neven,et al.  PhotoOCR: Reading Text in Uncontrolled Conditions , 2013, 2013 IEEE International Conference on Computer Vision.

[9]  Palaiahnakote Shivakumara,et al.  HMM-Based Multi Oriented Text Recognition in Natural Scene Image , 2013, 2013 2nd IAPR Asian Conference on Pattern Recognition.

[10]  Sanjeev Khudanpur,et al.  WEB-derived pronunciations , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

[12]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[13]  Jon Almazán,et al.  ICDAR 2013 Robust Reading Competition , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[14]  Jiri Matas,et al.  On Combining Multiple Segmentations in Scene Text Recognition , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[15]  Jerod J. Weinman,et al.  Toward Integrated Scene Text Reading , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[17]  Tatiana Novikova,et al.  Large-Lexicon Attribute-Consistent Text Recognition in Natural Images , 2012, ECCV.

[18]  Jeff A. Bilmes,et al.  Graphical models and automatic speech recognition , 2002 .

[19]  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.

[20]  Michael Collins,et al.  Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms , 2002, EMNLP.

[21]  Jacqueline L. Feild,et al.  Improving Text Recognition in Images of Natural Scenes , 2014 .