Radial Line Fourier Descriptor for Handwritten Word Representation

Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. The performance of information retrieval algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform and Speeded Up Robust Features, in order to represent handwritten words in a document, a robust descriptor is required that is not over-precise. This is because handwritten words across different documents are indeed similar, but not identical. Therefore, this paper introduces a Radial Line Fourier (RLF) descriptor for handwritten word feature representation, which is fast to construct and short-length with 32 elements only. The effectiveness of the proposed RLF descriptor is empirically evaluated using the VLFeat benchmarking framework (VLBenchmarks), and for handwritten word image representation using a historical marriage records dataset.

[1]  R. Weisberg.  A-N-D , 2011 .

[2]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[3]  Alicia Fornés,et al.  On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents , 2012, Int. J. Pattern Recognit. Artif. Intell..

[4]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[5]  Edwin R. Hancock,et al.  A statistical approach to sparse multi-scale phase-based stereo , 2007, Pattern Recognit..

[6]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[7]  Alicia Fornés,et al.  A Segmentation-Free Handwritten Word Spotting Approach by Relaxed Feature Matching , 2016, 2016 12th IAPR Workshop on Document Analysis Systems (DAS).

[8]  Konstantinos Zagoris,et al.  Unsupervised Word Spotting in Historical Handwritten Document Images Using Document-Oriented Local Features , 2017, IEEE Transactions on Image Processing.

[9]  Yuzuru Tanaka,et al.  Slit Style HOG Feature for Document Image Word Spotting , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[10]  Iasonas Kokkinos,et al.  Dense Segmentation-Aware Descriptors , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Frank Lebourgeois,et al.  Text search for medieval manuscript images , 2007, Pattern Recognit..

[12]  Alicia Fornés,et al.  The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition , 2013, Pattern Recognit..

[13]  Anders Hast,et al.  Automatic Document Image Binarization using Bayesian Optimization , 2017, HIP@ICDAR.

[14]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[15]  Basilios Gatos,et al.  A survey of document image word spotting techniques , 2017, Pattern Recognit..

[16]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Josep Lladós,et al.  Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method , 2011, 2011 International Conference on Document Analysis and Recognition.

[19]  Andrea Marchetti,et al.  Putative Match Analysis - A Repeatable Alternative to RANSAC for Matching of Aerial Images , 2012, VISAPP.

[20]  Nicholas R. Howe,et al.  Part-Structured Inkball Models for One-Shot Handwritten Word Spotting , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[21]  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).

[22]  B. S. Manjunath,et al.  A Mathematical Comparison of Point Detectors , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[23]  Iasonas Kokkinos,et al.  Scale invariance without scale selection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Adrien Bartoli,et al.  KAZE Features , 2012, ECCV.

[25]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[26]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Harish Srinivasan,et al.  Handwritten Arabic Word Spotting using the CEDARABIC Document Analysis System , 2005 .

[28]  Anders Hast,et al.  Invariant Interest Point Detection Based on Variations of the Spinor Tensor , 2014, WSCG 2014.

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

[30]  Alicia Fornés,et al.  A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance , 2014, 2014 22nd International Conference on Pattern Recognition.

[31]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[32]  Ernest Valveny,et al.  A Coarse-to-Fine Approach for Handwritten Word Spotting in Large Scale Historical Documents Collection , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[33]  F. Perronnin,et al.  Local gradient histogram features for word spotting in unconstrained handwritten documents , 2008 .

[34]  Adrien Bartoli,et al.  Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces , 2013, BMVC.

[35]  A. Yuille,et al.  Dense Scale Invariant Descriptors for Images and Surfaces , 2012 .

[36]  S. M. Steve.  SUSAN - a new approach to low level image processing , 1997 .

[37]  Ethan Rublee,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[38]  Alejandro Héctor Toselli,et al.  ICDAR2015 Competition on Keyword Spotting for Handwritten Documents , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[39]  Tobias Höllerer,et al.  Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking , 2011, International Journal of Computer Vision.

[40]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[41]  Andrea Marchetti,et al.  An Efficient Preconditioner and a Modified RANSAC for Fast and Robust Feature Matching. , 2012 .

[42]  Lior Wolf,et al.  A Simple and Fast Word Spotting Method , 2014, 2014 14th International Conference on Frontiers in Handwriting Recognition.

[43]  A. Papandreou,et al.  Slant estimation and core-region detection for handwritten Latin words , 2014, Pattern Recognit. Lett..

[44]  Anders Hast.  Robust and Invariant Phase Based Local Feature Matching , 2014, 2014 22nd International Conference on Pattern Recognition.

[45]  Ioannis Pratikakis,et al.  Segmentation-free Word Spotting in Historical Printed Documents , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[46]  Frank Lebourgeois,et al.  Towards an omnilingual word retrieval system for ancient manuscripts , 2009, Pattern Recognit..

[47]  Gustavo Carneiro,et al.  Multi-scale phase-based local features , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[48]  José A. Rodríguez-Serrano,et al.  Handwritten word-spotting using hidden Markov models and universal vocabularies , 2009, Pattern Recognit..

[49]  Núria Cirera,et al.  BH2M: The Barcelona Historical, Handwritten Marriages Database , 2014, 2014 22nd International Conference on Pattern Recognition.

[50]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

[51]  Anders Hast,et al.  Incremental Spherical Linear Interpolation , 2004 .