Layout Analysis of Arabic Script Documents

Layout analysis—extraction of text lines from a document image and identification of their reading order—is an important step in converting the document into a searchable electronic representation. Projection methods are typically employed for extraction of text lines in Arabic script documents. Although projection methods achieve good accuracy on clean, skew-free documents, their performance drops under challenging situations (border noise, skew, complex layouts, etc.). This chapter presents a layout analysis system for extracting text lines in reading order from scanned Arabic script document images written in different languages (Arabic, Urdu, Persian, etc.) and different styles (Naskh, Nastaliq, etc.). The presented system is based on a suitable combination of different well-established techniques for analyzing Latin script documents that have proven to be robust against different types of document image degradations.

[1]  Nasser Mozayani,et al.  A Persian OCR System Using Morphological Operators , 2007, WEC.

[2]  S. A. Husain.  A multi-tier holistic approach for Urdu Nastaliq recognition , 2002 .

[3]  Christoph H. Lampert,et al.  Internet: www.itwm.fraunhofer.de , 2022 .

[4]  Motoi Iwata,et al.  Segmentation of Page Images Using the Area Voronoi Diagram , 1998, Comput. Vis. Image Underst..

[5]  Friedrich M. Wahl,et al.  Document Analysis System , 1982, IBM J. Res. Dev..

[6]  Thomas M. Breuel,et al.  Two Geometric Algorithms for Layout Analysis , 2002, Document Analysis Systems.

[7]  Michael Riley.  Beyond quasi-stationarity: Designing time-frequency representations for speech signals , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Henry S. Baird,et al.  Truthing for Pixel-Accurate Segmentation , 2008, 2008 The Eighth IAPR International Workshop on Document Analysis Systems.

[9]  Stefano Messelodi,et al.  Geometric Layout Analysis Techniques for Document Image Understanding: a Review , 2008 .

[10]  U. Pal,et al.  Recognition of printed Urdu script , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[11]  Mohammad S. Khorsheed,et al.  Off-Line Arabic Character Recognition – A Review , 2002, Pattern Analysis & Applications.

[12]  Henry S. Baird.  Background Structure in Document Images , 1994, Int. J. Pattern Recognit. Artif. Intell..

[13]  Thomas M. Breuel,et al.  Performance Evaluation and Benchmarking of Six-Page Segmentation Algorithms , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Thomas M. Breuel,et al.  High Performance Document Layout Analysis , 2003 .

[15]  C. V. Jawahar,et al.  On Segmentation of Documents in Complex Scripts , 2007 .

[16]  Abderrazak Zahour,et al.  Arabic hand-written text-line extraction , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[17]  Lawrence O'Gorman,et al.  The Document Spectrum for Page Layout Analysis , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  James N. Damon,et al.  Properties of Ridges and Cores for Two-Dimensional Images , 1999, Journal of Mathematical Imaging and Vision.

[19]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[20]  M. Riley,et al.  Time-Frequency Representations for Speech Signals , 1987 .

[21]  Dan S. Bloomberg,et al.  Multiresolution Morphological Approach to Document Image Analysis , 1991 .

[22]  Volker Märgner,et al.  ICDAR 2009-Arabic handwriting recognition competition , 2011, 2011 International Conference on Document Analysis and Recognition.

[23]  S. Shirali-Shahreza,et al.  Page Segmentation of Persian/Arabic Printed Text Using Ink Spread Effect , 2006, 2006 SICE-ICASE International Joint Conference.

[24]  Bernie Mulgrew,et al.  Proceedings IEEE International Conference on Acoustics Speech and Signal Processing , 1991 .

[25]  David H. Eberly,et al.  Ridges for image analysis , 1994, Journal of Mathematical Imaging and Vision.

[26]  F. Shafait,et al.  Layout Analysis of Urdu Document Images , 2006, 2006 IEEE International Multitopic Conference.

[27]  R. K. Shyamasundar,et al.  Introduction to algorithms , 1996 .

[28]  Joost van de Weijer,et al.  Fast Anisotropic Gauss Filtering , 2002, ECCV.

[29]  Syed Saqib Bukhari,et al.  Document image segmentation using discriminative learning over connected components , 2010, DAS '10.

[30]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[31]  Mahesh Viswanathan,et al.  A prototype document image analysis system for technical journals , 1992, Computer.

[32]  Yalin Wang,et al.  IMPROVEMENT OF ZONE CONTENT CLASSIFICATION BY USING BACKGROUND ANALYSIS , 2000 .

[33]  Syed Saqib Bukhari,et al.  Script-Independent Handwritten Textlines Segmentation Using Active Contours , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[34]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[35]  Lawrence O'Gorman,et al.  Document Image Analysis , 1996 .

[36]  Tony Lindeberg,et al.  Edge Detection and Ridge Detection with Automatic Scale Selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[37]  Adel M. Alimi,et al.  Unsupervised Block Covering Analysis for Text-Line Segmentation of Arabic Ancient Handwritten Document Images , 2010, 2010 20th International Conference on Pattern Recognition.

[38]  Syed Saqib Bukhari,et al.  Improved document image segmentation algorithm using multiresolution morphology , 2011, Electronic Imaging.

[39]  Syed Saqib Bukhari,et al.  Ridges Based Curled Textline Region Detection from Grayscale Camera-Captured Document Images , 2009, CAIP.

[40]  George Nagy,et al.  Twenty Years of Document Image Analysis in PAMI , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Thomas M. Breuel,et al.  Document image zone classification - a simple high-performance approach , 2007, VISAPP.