This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Artificial Neural Networks for Document Analysis and Recognition
January 2005 (vol. 27 no. 1)
pp. 23-35
ASCII Text
x 
 
Simone Marinai, Marco Gori, Giovanni Soda, "Artificial Neural Networks for Document Analysis and Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 23-35, January, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.4,
author = {Simone Marinai and Marco Gori and Giovanni Soda},
title = {Artificial Neural Networks for Document Analysis and Recognition},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {1},
issn = {0162-8828},
year = {2005},
pages = {23-35},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.4},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Artificial Neural Networks for Document Analysis and Recognition
IS - 1
SN - 0162-8828
SP23
EP35
EPD - 23-35
A1 - Simone Marinai,
A1 - Marco Gori,
A1 - Giovanni Soda,
PY - 2005
KW - Character segmentation
KW - document image analysis and recognition
KW - layout analysis
KW - neural networks
KW - preprocessing
KW - recursive neural networks
KW - word recognition.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Marco Gori, IEEE
Giovanni Soda, IEEE Computer Society
Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.

[1] L. O'Gorman and R. Kasturi, Document Image Analysis. Los Alamitos, Calif.: IEEE Press, 1995.
[2] G. Nagy, “Twenty Years of Document Image Analysis in PAMI,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 38-62, Jan. 2000.
[3] R.G. Casey and E. Lecolinet, “A Survey of Methods and Strategies in Character Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 7, pp. 690-706, July 1996.
[4] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[5] M.D. Garris, C.L. Wilson, and J.L. Blue, “Neural Network-Based Systems for Handprint OCR Applications,” IEEE Trans. Image Processing, vol. 7, no. 8, pp. 1097-1112, 1998.
[6] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning Internal Representations by Error Propagation,” Parallel Distributed Processing, D.E. Rumelhart and J.L. McClelland, eds., vol. 1, ch. 8, pp. 318-362, 1986.
[7] P. Frasconi, M. Gori, and A. Sperduti, “A General Framework for Adaptive Processing of Data Structures,” IEEE Trans. Neural Networks, vol. 9, no. 5, pp. 768-786, 1998.
[8] P. Frasconi, M. Gori, and A. Sperduti, “Guest Editors' Introduction: Special Section on Connectionist Models for Learning in Structured Domains,” IEEE Trans. Knowledge and Data Eng., vol. 13, no. 2, pp. 145-147, Mar./Apr. 2001.
[9] M. Gori, S. Marinai, and G. Soda, “Artificial Neural Networks for Document Analysis and Recognition,” Technical Report N.1/2003, Univ. of Florence, http://www.dsi.unifi.itANNxDAR/, 2003.
[10] Z. Chi and K.W. Wong, “A Two-Stage Binarization Approach for Document Images,” Proc. Int'l Symp. Intelligent Multimedia, Video and Speech Processing (ISIMP '01), pp. 275-278, 2001.
[11] A.P. Whichello and H. Yan, “Linking Broken Character Borders with Variable Sized Masks to Improve Recognition,” Pattern Recognition, vol. 29, no. 8, pp. 1429-1435, 1996.
[12] P. Stubberud, J. Kanai, and V. Kalluri, “Adaptive Image Restoration of Text Images that Contain Touching or Broken Characters,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '95), pp. 778-781, 1995.
[13] P. Martin and C. Bellisant, “Low-Level Analysis of Music Drawing Images,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '91), pp. 417-425, 1991.
[14] N. Rondel and G. Burel, “Cooperation of Multilayer Perceptrons for the Estimation of Skew Angle in Text Document Images,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '95), pp. 1141-1144, 1995.
[15] R. Palaniappan, P. Raveendran, and S. Omatu, “New Invariant Moments for Non-Uniformly Scaled Images,” Pattern Analysis and Applications, vol. 3, no. 2, pp. 78-87, 2000.
[16] P. Ahmed, “A Neural Network Based Dedicated Thinning Method,” Pattern Recognition Letters, vol. 16, no. 6, pp. 585-590, 1995.
[17] T. Kohonen, “The Self-Organizing Map,” Proc. IEEE, vol. 78, no. 9, pp. 1464-1480, 1990.
[18] R. Singh, V. Cherkassky, and N. Papanikolopoulos, “Self-Organizing Maps for the Skeletonization of Sparse Shapes,” IEEE Trans. Neural Networks, vol. 11, no. 1 pp. 241-248, 2000.
[19] A. Datta, S.K. Parui, and B.B. Chaudhuri, “Skeletonization by a Topology-Adaptive Self-Organizing Neural Network,” Pattern Recognition, vol. 34, pp. 617-629, 2001.
[20] T. Mitchell, Machine Learning. McGraw-Hill, 1997.
[21] K. Suzuki, I. Horiba, and N. Sugie, “Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 12 pp. 1582-1596, Dec. 2003.
[22] D. Wang, A. Talevski, and T. Dillon, “Edge-Preserving Nonlinear Image Restoration Using Adaptive Components-Based Radial Basis Function Neural Networks,” Proc. Int'l Joint Conf. Neural Networks, vol. 2, pp. 1243-1248, 2003.
[23] K. Chen, D. Wang, and X. Liu, “Weight Adaptation and Oscillatory Correlation for Image Segmentation,” IEEE Trans. Neural Networks, vol. 11, no. 5 pp. 1106-1123, 2000.
[24] A.K. Jain and Y. Zhong, “Page Segmentation Using Texture Analysis,” Pattern Recognition, vol. 29, no. 5, pp. 743-770, 1996.
[25] C. Strouthopoulos and N. Papamarkos, “Text Identification for Document Image Analysis Using a Neural Network,” Image and Vision Computing, vol. 16, no. 12/13, pp. 879-896, 1998.
[26] D.X. Le, G.R. Thoma, and H. Wechsler, “Classification of Binary Document Images into Textual or Nontextual Data Blocks Using Neural Network Models,” Machine Vision and Applications, vol. 8, no. 5, pp. 289-504, 1995.
[27] S. Imade, S. Tatsuta, and T. Wada, “Segmentation and Classification for Mixed Text/Image Documents Using Neural Networks,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '93), pp. 930-934, 1993.
[28] K. Etemad, D.S. Doermann, and R. Chellappa, “Multiscale Segmentation of Unstructured Document Pages Using Soft Decision Integration,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 1, pp. 92-96, Jan. 1997.
[29] H. Yan and J. Wu, “Character and Line Extraction from Color Map Images Using a Multi-Layer Neural Network,” Pattern Recognition Letters, vol. 15, pp. 97-103, April 1994.
[30] K. Jung, “Neural Network-Based Text Location in Color Images,” Pattern Recognition Letters, vol. 22, pp. 1503-1515, 2001.
[31] C. Strouthopoulos, N. Papamarkos, and A. Atsalakis, “Text Extraction in Complex Color Documents,” Pattern Recognition, vol. 35, pp. 1743-1758, 2002.
[32] G.S. Palmero and Y. Dimitriadis, “Structured Document Labeling and Rule Extraction Using New Recurrent Fuzzy-Neural Systems,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '99), pp. 181-184, 1999.
[33] W.P. de Waard, “Neural Techniques and Postal Code Detection,” Pattern Recognition Letters, vol. 15, no. 2, pp. 199-206, 1994.
[34] S.L. Taylor, R. Fritzson, and J.A. Pastor, “Extraction of Data from Preprinted Forms,” Machine Vision and Applications, vol. 5, no. 5, pp. 211-222, 1992.
[35] F. Cesarini, M. Lastri, S. Marinai, and G. Soda, “Encoding of Modified X-Y Trees for Document Classification,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '01), pp. 1131-1136, 2001.
[36] K.Y. Wong, R.G. Casey, and F.M. Wahl, “Document Analysis System,” IBM J. Research and Development, vol. 26, no. 6, pp. 647-656, 1982.
[37] F.Y. Shih and S.S. Chen, “Adaptive Document Block Segmentation and Classification,” IEEE Trans. Systems, Man, and Cybernetics, vol. 26, no. 5, pp. 797-802, 1996.
[38] G.A. Carpenter, S. Grossberg, N. Markuzon, J.H. Reynolds, and D.B. Rosen, “Fuzzy ARTMAP: A Neural Network Architecture for Incremental Learning of Analog Multidimensional Maps,” IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 698-713, 1992.
[39] J. Hu, R. Kashi, and G. Wilfong, “Document Image Layout Comparison and Classification,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '99), pp. 285-288, 1999.
[40] J. Yuan, Y.Y. Tang, and C.Y. Suen, “Four Directional Adjacency Graphs (FDAG) and Their Application in Locating Fields in Forms,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '95,) pp. 752-755, 1995.
[41] G. Nagy and S. Seth, “Hierarchical Representation of Optically Scanned Documents,” Proc. Int'l Conf. Pattern Recognition (ICPR '84), pp. 347-349, 1984.
[42] A.K. Jain and B. Yu, “Document Representation and Its Application to Page Decomposition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 294-308, Mar. 1998.
[43] J. Wang and J. Jean, “Segmentation of Merged Characters by Neural Networks and Shortest Path,” Pattern Recognition, vol. 27, no. 5, pp. 649-658, 1994.
[44] Z.K. Lu, Z. Chi, and W.C. Siu, “Length Estimation of Digits Strings Using a Neural Network with Structure Based Features,” SPIE/IS&T J. Electronic Imaging, vol. 7, pp. 79-85, Jan. 1998.
[45] B. Eastwood, A. Jennings, and A. Harvey, “A Low Level Feature Based Neural Network Segmenter for Fully Cursive Handwritten Words,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '97), p. 523, 1997.
[46] D. You and G. Kim, “An Approach for Locating Segmentation Points of Handwritten Digit Strings Using a Neural Network,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '03), pp. 142-146, 2003.
[47] J.H. Bae, K. Jung, J. Kim, and H. Kim, “Segmentation of Touching Characters Using an MLP,” Pattern Recognition Letters, vol. 19, no. 8, pp. 701-709, 1998.
[48] S.W. Lee, D.-J. Lee, and H.-S. Park, “A New Methodology for Gray-Scale Character Segmentation and Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp. 1045-1050, Oct. 1996.
[49] D.J. Burr, “Experiments on Neural Net Recognition of Spoken and Written Text,” IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 36, no. 7, pp. 1162-1168, 1988.
[50] W. Utschick, P. Nachbar, C. Knobloch, and J.A. Nossek, “The Evaluation of Feature Extraction Criteria Applied to Neural Network Classifiers,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '95), pp. 315-318, 1995.
[51] K. Fukushima and N. Wake, “Handwritten Alphanumeric Character Recognition by the Neocognitron,” IEEE Trans. Neural Networks, vol. 2, no. 3, pp. 355-365, 1991.
[52] M. Gori, M. Maggini, S. Marinai, J. Sheng, and G. Soda, “Edge-Backpropagation for Noisy Logo Recognition,” Pattern Recognition, vol. 36, no. 1 pp. 103-110, 2003.
[53] A. Amin, H. Al-sadoun, and S. Fischer, “Hand-Printed Arabic Character Recognition System Using an Artificial Network,” Pattern Recognition, vol. 29, no. 4, pp. 663-675, 1996.
[54] L. Cordella, C. De Stefano, and M. Vento, “A Neural Network Classifier for OCR Using Structural Descriptions,” Machine Vision and Applications, vol. 8, no. 5, pp. 336-342, 1995.
[55] M. Diligenti, M. Gori, M. Maggini, and E. Martinelli, “Adaptive Graphical Pattern Recognition for the Classification of Company Logos,” Pattern Recognition, vol. 34, pp. 2049-2061, 2001.
[56] G. Cortelazzo, G.A. Mian, G. Vezzi, and P. Zamperoni, “Trademark Shapes Description by String-Matching Techniques,” Pattern Recognition, vol. 27, no. 8, pp. 1005-1018, 1994.
[57] N.W. Strathy and C.Y. Suen, “A New System for Reading Handwritten Zip Codes,” Proc. Int'l Conf. Document Analysisand Recognition (ICDAR '95), pp. 74-77, 1995.
[58] D.-S. Lee and S.N. Srihari, “Dynamic Classifier Combination Using Neural Network,” Proc. SPIE - Document Recognition II, pp. 26-37, 1995.
[59] L. Mui, A. Agarwal, A. Gupta, and P.S.-P. Wang, “An Adaptive Modular Neural Network with Application to Unconstrained Character Recognition,” Int'l J. Pattern Recognition and Artificial Intelligence, vol. 8, no. 5, pp. 1189-1204, 1994.
[60] J. Mao, K. Mohiuddin, and T. Fujisaki, “A Two-Stage Multi-Network OCR System with a Soft Pre-Classifier and a Network Selector,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '95), pp. 78-81, 1995.
[61] S.-B. Cho and J.H. Ki, “Recognition of Large-Set Printed Hangul (Korean Script) by Two-Stage Backpropagation Neural Classifier,” Pattern Recognition, vol. 25, no. 11, pp. 1353-1360, 1992.
[62] H. Takahashi and T.D. Griffin, “Recognition Enhancement by Linear Tournament Verification,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '93), pp. 585-588, 1993.
[63] E. Francesconi, M. Gori, S. Marinai, and G. Soda, “A Serial Combination of Connectionist-Based Classifiers for OCR,” Int'l J. Document Analysis and Recognition, vol. 3, no. 3 pp. 160-168, 2001.
[64] R.Y.-M. Teo and R. Shingal, “A Hybrid Classifier for Recognizing Handwritten Numerals,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '97), pp. 283-287, 1997.
[65] H. Su, W. Wang, X. Li, and S. Xia, “Hierarchical Neural Network for Recognizing Hand-Written Characters in Engineering Drawings,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '95), pp. 46-49, 1995.
[66] J.H. Kim, K.K. Kim, and C.Y. Suen, “An HMM-MLP Hybrid Model for Cursive Script Recognition,” Pattern Analysis and Applications, vol. 3, no. 4, pp. 314-324, 2000.
[67] F. Cesarini, M. Gori, S. Marinai, and G. Soda, “INFORMys: A Flexible Invoice-Like Form Reader System,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 7, pp. 730-745, July 1998.
[68] T. Steinherz, E. Rivlin, and N. Intrator, “Offline Cursive Script Word Recognition–A Survey,” Int'l J. Document Analysis and Recognition, vol. 2, nos. 2/3, pp. 90-110, 1999.
[69] P.D. Gader, M. Mohamed, and J.-H. Chiang, “Comparison of Crisp and Fuzzy Character Neural Networks in Handwritten Word Recognition,” IEEE Trans. Fuzzy Systems, vol. 3, no. 3, pp. 357-363, 1995.
[70] J. Bromley and J.S. Denker, “Improving Rejection Performance on Handwritten Digits by Training with Rubbish,” Neural Computation, vol. 5, no. 3, pp. 367-370, 1993.
[71] P. Gader, M. Mohamed, and H. Chiang, “Handwritten Word Recognition with Character and Inter-Character Neural Networks,” IEEE Trans. Systems, Man, and Cybernetics, pp. 158-164, 1997.
[72] G. Martin, “Centered-Object Integrated Segmentation and Recognition of Overlapping Handprinted Characters,” Neural Computation, vol. 5, no. 3, pp. 419-429, 1993.
[73] F. Leclerc and R. Plamondon, “Automatic Signature Verification: The State of the Art,” Int'l J. Pattern Recognition and Artificial Intelligence, vol. 8, no. 3, pp. 643-660, 1994.
[74] J.P. Drouhard, R. Sabourin, and M. Godbout, “A Neural Network Approach to Off-Line Signature Verification Using Directional PDF,” Pattern Recognition, vol. 29, no. 3, pp. 415-424, 1996.
[75] R. Plamondon and G. Lorette, “Automatic Signature Verification and Writer Identification: The State of the Art,” Pattern Recognition, vol. 22, no. 2, pp. 107-131, 1989.
[76] N.A. Murshed, F. Bortolozzi, and R. Sabourin, “Off-Line Signature Verification, without A Priori Knowledge of Class $\omega_2$ . A New Approach,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '95), pp. 191-195, 1995.
[77] H. Cardot, M. Revenu, B. Victorri, and M.-J. Revillet, “A Static Signature Verification System Based on a Cooperating Neural Networks Architecture,” Int'l J. Pattern Recognition and Artificial Intelligence, vol. 8, no. 3, pp. 679-692, 1994.
[78] U.-V. Marti, R. Messerli, and H. Bunke, “Writer Identification Using Text Line Based Features,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '01), pp. 101-105, 2001.
[79] M. Gori and F. Scarselli, “Are Multilayer Perceptrons Adequate for Pattern Recognition and Verification?” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 10, pp. 1121-1132, Oct. 1998.
[80] J. Liu and P. Gader, “Neural Networks with Enhanced Outlier Rejection Ability for Off-Line Handwritten Word Recognition,” Pattern Recognition, vol. 35, pp. 2061-2071, 2002.
[81] P.Y. Simard, D. Steinkraus, and J.C. Platt, “Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '03), pp. 958-963, 2003.
[82] M. Bianchini, P. Frasconi, and M. Gori, “Learning in Multilayered Networks Used as Autoassociators,” IEEE Trans. Neural Networks, vol. 6, no. 2, pp. 512-515, 1995.
[83] P. Frasconi, M. Gori, and G. Soda, “Links between LVQ and Backpropagation,” Pattern Recognition Letters, vol. 18, no. 4, pp. 303-310, 1997.
[84] R. Raina, Y. Shen, A.Y. Ng, and A. McCallum, “Classification with Hybrid Generative/Discriminative Models,” Advances in Neural Information Processing Systems 16, S. Thrun, L. Saul, and B. Schölkopf, eds., Cambridge, Mass.: MIT Press, 2004.
[85] C.-L. Liu, H. Sako, and H. Fujisawa, “Discriminative Learning Quadratic Discriminant Function for Handwriting Recognition,” IEEE Trans. Neural Networks, vol. 15, no. 2, pp. 430-444, 2004.
[86] C.-L. Liu, K. Nakashima, H. Sako, and H. Fujisawa, “Handwritten Digit Recognition: Benchmarking of State-of-the-Art Techniques,” Pattern Recognition, vol. 36, pp. 2271-2285, 2003.
[87] S. Marinai, E. Marino, and G. Soda, “Indexing and Retrieval of Words in Old Documents,” Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '03), pp. 223-227, 2003.
[88] E. Trentin and M. Gori, “Robust Combination of Neural Networks and Hidden Markov Models for Speech Recognition,” IEEE Trans. Neural Networks, vol. 14, no. 6, pp. 1519-1531, 2003.
[89] P. Frasconi, M. Gori, A. Kuechler, and A. Sperduti, “From Sequences to Data Structures: Theory and Applications,” A Field Guide to Dynamical Recurrent Networks, J. Kolen and S. Kremer, eds., New York: IEEE Press, 2001.
[90] T. Caelli, W.F. Bischof, and M. Ferraro, “A Comparison of Neural and Graphical Models for Syntactic and Structural Pattern Recognition,” Proc. First IAPR-TC3 Artificial Neural Networks in Pattern Recognition Workshop (ANNPR '03), M. Gori and S. Marinai, eds., pp. 1-7, 2003.
[91] J.-X. Dong, C. Suen, and A. Krzyzak, “High Accuracy Handwritten Chinese Character Recognition Using Support Vector Machine,” Proc. First IAPR-TC3 Artificial Neural Networks in Pattern Recognition Workshop (ANNPR '03), M. Gori and S. Marinai, eds., pp. 39-45, 2003.
[92] L. Zeyu, S. Tang, and H. Wang, “Fast Recognition of Handwritten Digits Using Pairwise Coupling Support Vector Machine,” Proc. Int'l Joint Conf. Neural Networks, vol. 1, pp. 878-883, 2002.
[93] H.-S. Wong and L. Guan, “A Neural Learning Approach for Adaptive Image Restoration Using a Fuzzy Model-Based Network Architecture,” IEEE Trans. Neural Networks, vol. 12, no. 3, pp. 516-531, 2001.
[94] S. Lu, Z. Wang, and J. Shen, “Neuro-Fuzzy Synergism to the Intelligent System for Edge Detection and Enhancement,” Pattern Recognition, vol. 36, pp. 2395-2409, 2003.
[95] D.-C. Park and Y.-J. Woo, “Weighted Centroid Neural Network for Edge Preserving Image Compression,” IEEE Trans. Neural Networks, vol. 12, no. 5, pp. 1134-1146, 2001.
[96] D. Feiden and R. Tetzlaff, “Binary Image Coding Using Cellular Neural Networks,” Proc. Int'l Joint Conf. Neural Networks, pp. 1149-1152, 2003.
[97] K.I. Kim, K. Jung, S.H. Park, and H.J. Kim, “Support Vector Machines for Texture Classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 11, pp. 1542-1550, Nov. 2002.
[98] P. Clark and M. Mirmehdi, “Combining Statistical Measures to Find Image Text Regions,” Proc. Int'l Conf. Production Research (ICPR '00), pp. 450-453, 2000.
[99] E. Lopez-Rubio, J. Munoz-Perez, and J. Gomez-Ruiz, “A Robust Two-Stage System for Image Segmentation,” Proc. Int'l Conf. Production Research (ICPR '00), pp. 606-609, 2000.
[100] X. Feng, A.K. I. Williams, and S. Felderhof, “Combining Belief Networks and Neural Networks for Scene Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 467-483, Apr. 2002.
[101] G. Karystinos and D. Pados, “On Overfitting, Generalization, and Randomly Expanded Training Sets,” IEEE Trans. Neural Networks, vol. 11, no. 5, pp. 1050-1057, 2000.
[102] J. Ghosn and Y. Bengio, “Bias Learning, Knowledge Sharing,” IEEE Trans. Neural Networks, vol. 14, no. 4, pp. 748-765, 2003.

Index Terms:
Character segmentation, document image analysis and recognition, layout analysis, neural networks, preprocessing, recursive neural networks, word recognition.
Citation:
Simone Marinai, Marco Gori, Giovanni Soda, "Artificial Neural Networks for Document Analysis and Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 23-35, Jan. 2005, doi:10.1109/TPAMI.2005.4
Usage of this product signifies your acceptance of the Terms of Use.