Abstract
This chapter provides an overview of document image analysis (DIA). It aims to provide fundamental issues/techniques related to DIA, such textual processing and graphics processing. The chapter focusses on how research scientists, academicians and industrialists see the phrase DIA, and how have they approached since several years. At the end, the importance of graphics recognition has been clearly outlined. Note that the core idea of the chapter is inspired from previous work.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Detecting a graphical symbol ‘logo’ can be used to provide an example to avoid complexity document retrieval process. In document understanding, there exist several ways to do exactly similar task: document identification. For more information, follow Sect. 1.2.
References
R. Kasturi, L. O’Gorman, V. Govindaraju, Document image analysis: a primer. Character Recognit. 27(1), 3–22 (2002)
B. Klein, S. Agne, A. Dengel, Results of a study on invoice-reading systems in Germany, in Simone Marinai and Andreas Dengel. Proceedings of International Workshop on Document Analysis Systems. Lecture Notes in Computer Science, vol. 3163 (Springer, Berlin, 2004), pp. 451–462
K.C. Santosh, A. Belaïd, Document information extraction and its evaluation based on client’s relevance, in 12th International Conference on Document Analysis and Recognition (2013), pp. 35–39
K.C. Santosh, A. Belaïd, Client-driven content extraction associated with table, in Proceedings of the 13th IAPR International Conference on Machine Vision Applications (2013), pp. 277–280
K.C. Santosh, A. Belaïd, Pattern-based approach to table extraction, in Pattern Recognition and Image Analysis - 6th Iberian Conference, IbPRIA 2013, Funchal, Madeira, Portugal, June 5–7, 2013. Proceedings (2013), pp. 766–773
K.C. Santosh, g-DICE: graph mining-based document information content exploitation. Int. J. Doc. Anal. Recognit. (IJDAR) 18(4), 337–355 (2015)
L.A. Fletcher, R. Kasturi, A robust algorithm for text string separation from mixed text/graphics images. IEEE Trans. Pattern Anal. Mach. Intell. 10(6), 910–918 (1988)
G. Nagy, Twenty years of document image analysis in PAMI. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 38–62 (2000)
A. Dengel, G. Barth, Anastasil: hybrid knowledge-based system for document layout analysis, in Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI’89), vol. 2 (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1989), pp. 1249–1254
H.S. Baird, Anatomy of a versatile page reader. Proc. IEEE 80(7), 1059–1065 (1992)
L. O’Gorman, The document spectrum for page layout analysis. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1162–1173 (1993)
S.-W. Lee, D.-S. Ryu, Parameter-free geometric document layout analysis. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1240–1256 (2001)
F. Shafait, Geometric layout analysis of scanned documents. PhD thesis, Kaiserslautern University of Technology, Germany (2008)
A.L. Spitz, Correcting for variable skew, in Proceedings of the 5th IAPR International Workshop on Document Analysis Systems, Princeton, NJ (USA), ed. by D. Lopresti, J. Hu, R. Kashi. Lecture Notes in Computer Science, vol. 2423 (Springer, Berlin, 2002), pp. 179–187
A. Belaïd, K.C. Santosh, V. Poulain D’Andecy, Handwritten and printed text separation in real document, in Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013, Kyoto, Japan, May 20–23, 2013 (2013), pp. 218–221
X. Peng, S. Setlur, V. Govindaraju, R. Sitaram, Handwritten text separation from annotated machine printed documents using Markov Random Fields. Int. J. Doc. Anal. Recognit. (IJDAR) 16(1), 1–16 (2013)
A. Alaei, M. Delalandre, A complete logo detection/recognition system for document images, in 2014 11th IAPR International Workshop on Document Analysis Systems (2014), pp. 324–328
R. Jain, D. Doermann, Logo retrieval in document images, in 2012 10th IAPR International Workshop on Document Analysis Systems (2012), pp. 135–139
A. Alaei, P.P. Roy, U. Pal, Logo and seal based administrative document image retrieval: a survey. Comput. Sci. Rev. 22, 47–63 (2016)
K. Ubul, G. Tursun, A. Aysa, D. Impedovo, G. Pirlo, T. Yibulayin, Script identification of multi-script documents: a survey. IEEE Access 5, 6546–6559 (2017)
Sk Md Obaidullah, C. Halder, K.C. Santosh, N. Das, K. Roy, PHDIndic\_11: page-level handwritten document image dataset of 11 official indic scripts for script identification. Multimedia Tools Appl. 77(2), 1643–1678 (2018)
Sk Md Obaidullah, K.C. Santosh, C. Halder, N. Das, K. Roy, Automatic indic script identification from handwritten documents: page, block, line and word-level approach. Int. J. Mach. Learn. Cybern. (2017)
F. Shafait, J. van Beusekom, D. Keysers, T.M. Breuel, Document cleanup using page frame detection. Int. J. Doc. Anal. Recognit. (IJDAR) 11(2), 81–96 (2008)
F. Shafait, T.M. Breuel, The effect of border noise on the performance of projection-based page segmentation methods. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 846–851 (2011)
M. Agrawal, D. Doermann, Clutter noise removal in binary document images. Int. J. Doc. Anal. Recognit. (IJDAR) 16(4), 351–369 (2013)
R. Martens, L. Claesen, Incorporating local consistency information into the online signature verification process. Int. J. Doc. Anal. Recognit. 1(2), 110–115 (1998)
R. Jayadevan, S.R. Kolhe, P.M. Patil, U. Pal, Automatic processing of handwritten bank cheque images: a survey. Int. J. Doc. Anal. Recognit. (IJDAR) 15(4), 267–296 (2012)
D. Rivard, E. Granger, R. Sabourin, Multi-feature extraction and selection in writer-independent off-line signature verification. Int. J. Doc. Anal. Recognit. (IJDAR) 16(1), 83–103 (2013)
M. Coustaty, Contribution à l’analyse complexe de documents anciens, application aux lettrines. (Complex analysis of historical documents, application to lettrines). PhD thesis, University of La Rochelle, France (2011)
M. Coustaty, R. Pareti, N. Vincent, J.-M. Ogier, Towards historical document indexing: extraction of drop cap letters. IJDAR 14(3), 243–254 (2011)
M. Coustaty, K. Bertet, M. Visani, J.-M. Ogier, A new adaptive structural signature for symbol recognition by using a Galois lattice as a classifier. IEEE Trans. Syst. Man Cybern. Part B 41(4), 1136–1148 (2011)
M. Clément, M. Coustaty, C. Kurtz, L. Wendling, Local enlacement histograms for historical drop caps style recognition, in 14th IAPR International Conference on Document Analysis and Recognition (2017), pp. 299–304
Y.-Y. Chiang, S. Leyk, C.A. Knoblock, A survey of digital map processing techniques. ACM Comput. Surv. 47(1), 1:1–1:44 (2014)
Y.-Y. Chiang, S. Leyk, N.H. Nazari, S. Moghaddam, T.X. Tan, Assessing the impact of graphical quality on automatic text recognition in digital maps. Comput. Geosci. 93(C), 21–35 (2016)
Y.-Y. Chiang, C.A. Knoblock, Recognizing text in raster maps. Geoinformatica 19(1), 1–27 (2015)
J.H. Uhl, Extracting human settlement footprint from historical topographic map series using context-based machine learning. IET Conf. Proc. (2017), pp. 15(6 .)–15 (6 .)(1)
Y.-Y. Chiang, Unlocking textual content from historical maps - potentials and applications, trends, and outlooks, in Recent Trends in Image Processing and Pattern Recognition, ed. by K.C. Santosh, M. Hangarge, V. Bevilacqua, A. Negi (Singapore, 2017), pp. 111–124
G. Nagy, A. Samal, S. Seth, T. Fisher, E. Guthmann, K. Kalafala, L. Li, S. Sivasubramaniam, Y. Xu, Reading street names from maps - technical challenges, in GIS/LIS (1997)
T. Kaneko, Line structure extraction from line-drawing images. Pattern Recognit. 25(9), 963–973 (1992)
K. Tombre, S. Tabbone, L. Pélissier, B. Lamiroy, Ph. Dosch, Text/graphics separation revisited, in Proceedings of the 5th IAPR International Workshop on Document Analysis Systems, Princeton, NJ (USA), ed. by D. Lopresti, J. Hu, R. Kashi. Lecture Notes in Computer Science, vol. 2423 (Springer, Berlin, 2002), pp. 200–211
M. Delalandre, E. Valveny, T. Pridmore, D. Karatzas, Generation of synthetic documents for performance evaluation of symbol recognition & spotting systems. Int. J. Doc. Anal. Recognit. 13(3), 187–207 (2010)
W. Min, Z. Tang, L. Tang, Recognition of dimensions in engineering drawings based on arrowhead-match, in Proceedings of 2nd International Conference on Document Analysis and Recognition, Tsukuba (Japan) (1993), pp. 373–376
L. Wendling, S. Tabbone, Recognition of arrows in line drawings based on the aggregation of geometric criteria using the Choquet integral, in Proceedings of 7th International Conference on Document Analysis and Recognition, Edinburgh (Scotland, UK) (2003), pp. 299–303
L. Wendling, S. Tabbone, A new way to detect arrows in line drawings. IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 935–941 (2004)
B.B. Chaudhuri, Digital Document Processing: Major Directions and Recent Advances (Advances in Pattern Recognition) (Springer, New York, 2006)
M. Rusiñol, J. Lladós, Symbol Spotting in Digital Libraries: Focused Retrieval over Graphic-rich Document Collections (Springer, London, 2010)
K.C. Santosh, L. Wendling, B. Lamiroy, Bor: Bag-of-relations for symbol retrieval. Int. J. Pattern Recognit. and Artif. Intell. 28(06), 1450017 (2014)
L.-P. de las Heras, S. Ahmed, M. Liwicki, E. Valveny, G. Sánchez, Statistical segmentation and structural recognition for floor plan interpretation. Int. J. Doc. Anal. Recognit. (IJDAR) 17(3), 221–237 (2014)
D. Camozzato, L. Dihl, I. Silveira, F. Marson, S.R. Musse, Procedural floor plan generation from building sketches. Vis. Comput. 31(6–8), 753–763 (2015)
G. Priestnall, R.E. Marston, D.G. Elliman, Arrowhead recognition during automated data capture. Pattern Recognit. Lett. 17(3), 277–286 (1996)
K.C. Santosh, B. Lamiroy, J.-P. Ropers, Inductive logic programming for symbol recognition, in Proceedings of International Conference on Document Analysis and Recognition (IEEE Computer Society, 2009), pp. 1330–1334
K.C. Santosh, Reconnaissance graphique en utilisant les relations spatiales et analyse de la forme. (Graphics Recognition using Spatial Relations and Shape Analysis). PhD thesis, University of Lorraine, France (2011)
K.C. Santosh, B. Lamiroy, L. Wendling, Spatio-structural symbol description with statistical feature add-on, in Graphics Recognition. New Trends and Challenges, ed. by Y.-B. Kwon, J.-M. Ogier, Lecture Notes, in Computer Science, vol. 7423, (Springer, Berlin, 2011), pp. 228–237
K.C. Santosh, B. Lamiroy, L. Wendling, Symbol recognition using spatial relations. Pattern Recognit. Lett. 33(3), 331–341 (2012)
K.C. Santosh, L. Wendling, B. Lamiroy, Relation bag-of-features for symbol retrieval, in 12th International Conference on Document Analysis and Recognition (2013), pp. 768–772
K.C. Santosh, B. Lamiroy, L. Wendling, DTW-radon-based shape descriptor for pattern recognition. Int. J. Pattern Recognit. Artificial Intell. 27(3), 1350008 (2013)
K.C. Santosh, L. Wendling, Graphical Symbol Recognition (Wiley, New York, 2015), pp. 1–22
K.C. Santosh, Complex and composite graphical symbol recognition and retrieval: a quick review, in Recent Trends in Image Processing and Pattern Recognition, Revised Selected Papers, ed. by K.C. Santosh, M. Hangarge, V. Bevilacqua, A. Negi Communications in Computer and Information. Science 709, 3–15 (2017)
K.C. Santosh, B. Lamiroy, L. Wendling, Integrating vocabulary clustering with spatial relations for symbol recognition. Int. J. Doc. Anal. Recognit. 17(1), 61–78 (2014)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Santosh, K.C. (2018). Document Image Analysis. In: Document Image Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-13-2339-3_1
Download citation
DOI: https://doi.org/10.1007/978-981-13-2339-3_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2338-6
Online ISBN: 978-981-13-2339-3
eBook Packages: Computer ScienceComputer Science (R0)