A Simple and Effective Solution for Script Identification in the Wild

We present an approach for automatically identifying the script of the text localized in the scene images. Our approach is inspired by the advancements in mid-level features. We represent the text images using mid-level features which are pooled from densely computed local features. Once text images are represented using the proposed mid-level feature representation, we use an off-the-shelf classifier to identify the script of the text image. Our approach is efficient and requires very less labeled data. We evaluate the performance of our method on a recently introduced CVSI dataset, demonstrating that the proposed approach can correctly identify script of 96.70% of the text images. In addition, we also introduce and benchmark a more challenging Indian Language Scene Text (ILST) dataset for evaluating the performance of our method.

[1]  Jean Ponce,et al.  Learning mid-level features for recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Umapada Pal,et al.  Two-stage Approach for Word-wise Script Identification , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[3]  Manik Varma,et al.  Character Recognition in Natural Images , 2009, VISAPP.

[4]  Shijian Lu,et al.  Video Script Identification Based on Text Lines , 2011, 2011 International Conference on Document Analysis and Recognition.

[5]  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.

[6]  Tinne Tuytelaars,et al.  Mining Mid-level Features for Image Classification , 2014, International Journal of Computer Vision.

[7]  Alexei A. Efros,et al.  Unsupervised Discovery of Mid-Level Discriminative Patches , 2012, ECCV.

[8]  C. V. Jawahar,et al.  Blocks That Shout: Distinctive Parts for Scene Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Dimosthenis Karatzas,et al.  A fast hierarchical method for multi-script and arbitrary oriented scene text extraction , 2014, International Journal on Document Analysis and Recognition (IJDAR).

[10]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Xiang Bai,et al.  Script identification in the wild via discriminative convolutional neural network , 2016, Pattern Recognit..

[12]  Debashis Ghosh,et al.  Script Recognition—A Review , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Umapada Pal,et al.  ICDAR2015 Competition on Video Script Identification (CVSI 2015) , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[14]  A. G. Ramakrishnan,et al.  Word level multi-script identification , 2008, Pattern Recognit. Lett..

[15]  Jayanthi Sivaswamy,et al.  A generalised framework for script identification , 2007, International Journal of Document Analysis and Recognition (IJDAR).

[16]  Volkmar Frinken,et al.  A Novel Word Spotting Method Based on Recurrent Neural Networks , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  C. V. Jawahar,et al.  Scene Text Recognition using Higher Order Language Priors , 2009, BMVC.

[18]  C. V. Jawahar,et al.  Can RNNs reliably separate script and language at word and line level? , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[19]  Feiyue Huang,et al.  Automatic script identification in the wild , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).