Text localization in natural images through effective re-identification of the MSER

Text detection and recognition from images have numerous applications for document analysis and information retrieval tasks. An accurate and robust method for detecting texts in natural scene images is proposed in this paper. Text-region candidates are detected using maximally stable extremal regions (MSER) and a machine learning based method is then applied to refine and validate the initial detection. The effectiveness of features based on aspect ratio, GLSM, LBP, HOG descriptors are investigated. Text-region classifiers of MLP, SVM and RF are trained using selections of these features and their combination. A publicly available multilingual dataset ICDAR 2003,2011 has been used to evaluate the method. The proposed method achieved excellent performance on both databases and the improvements are significant in terms of Precision, Recall, and F-measure. The results show that using a suitable feature combination and selection approach can can significantly increase the accuracy of the algorithms. Keywords---text detection; scene images; ICDAR; feature selection.

[1]  Luis Miguel Bergasa,et al.  Text location in complex images , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[2]  Jin Tae Kwak,et al.  Efficient data mining for local binary pattern in texture image analysis , 2015, Expert Syst. Appl..

[3]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Ujjwal Bhattacharya,et al.  Scene text detection using sparse stroke information and MLP , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

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

[6]  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 .

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

[8]  P. S. Hiremath,et al.  Multilingual Text Localization in Natural Scene Images using Wavelet based Edge Features and Fuzzy Classification , 2015 .

[9]  S. Lucas,et al.  ICDAR 2003 robust reading competitions: entries, results, and future directions , 2005, International Journal of Document Analysis and Recognition (IJDAR).

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

[11]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[12]  Jingmin Xin,et al.  Natural scene text detection with multi-layer segmentation and higher order conditional random field based analysis , 2015, Pattern Recognit. Lett..

[13]  Dimosthenis Karatzas,et al.  Multi-script Text Extraction from Natural Scenes , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[14]  Yingli Tian,et al.  Localizing Text in Scene Images by Boundary Clustering, Stroke Segmentation, and String Fragment Classification , 2012, IEEE Transactions on Image Processing.

[15]  Nikita P. Desai,et al.  Text Detection and Recognition in images: A survey , 2018, ArXiv.

[16]  Ulrich Simon.  The Rebirth of Tragedy , 1989 .

[17]  Jorge Stolfi,et al.  Text detection and recognition in urban scenes , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

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

[19]  Jin Hyung Kim,et al.  Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

[22]  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.

[23]  Huizhong Chen,et al.  Robust text detection in natural images with edge-enhanced Maximally Stable Extremal Regions , 2011, 2011 18th IEEE International Conference on Image Processing.

[24]  Cheng-Lin Liu,et al.  A Hybrid Approach to Detect and Localize Texts in Natural Scene Images , 2011, IEEE Transactions on Image Processing.

[25]  David S. Doermann,et al.  Text Detection and Recognition in Imagery: A Survey , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  David A Clausi.  An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .

[27]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..

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