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2017 | 27 | 1 | 195-206

Tytuł artykułu

Projection-based text line segmentation with a variable threshold

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
Document image segmentation into text lines is one of the stages in unconstrained handwritten document recognition. This paper presents a new algorithm for text line separation in handwriting. The developed algorithm is based on a method using the projection profile. It employs thresholding, but the threshold value is variable. This permits determination of low or overlapping peaks of the graph. The proposed technique is shown to improve the recognition rate relative to traditional methods. The algorithm is robust in text line detection with respect to different text line lengths.

Rocznik

Tom

27

Numer

1

Strony

195-206

Opis fizyczny

Daty

wydano
2017
otrzymano
2016-01-17
poprawiono
2016-08-16
poprawiono
2016-10-17
zaakceptowano
2016-10-24

Twórcy

autor
  • Department of Computer Engineering, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland
  • Department of Computer Engineering, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland
  • Department of Computer Engineering, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland

Bibliografia

  • Alaei, A., Nagabhushan, P. and Pal, U. (2011). Piece-wise painting technique for line segmentation of unconstrained handwritten text: A specific study with Persian text documents, Pattern Analysis and Applications 14(4): 381-394.
  • Antonacopoulos, A. and Karatzas, D. (2004). Document image analysis for World War II personal records, International Workshop on Document Image Analysis for Libraries, 2004, Palo Alto, CA, USA, pp. 336-341.
  • Arlot, S. and Celisse, A. (2010). A survey of cross-validation procedures for model selection, Statistics Surveys 4: 40-79.
  • Basu, S., Chaudhuri, C., Kundu, M., Nasipuri, M. and Basu, D.K. (2007). Text line extraction from multi-skewed handwritten documents, Pattern Recognition 40(6): 1825-1839.
  • Bouckaert, R.R. and Frank, E. (2004). Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms, Springer, Berlin/Heidelberg, pp. 3-12.
  • Brodić, D. (2012). Extended approach to water flow algorithm for text line segmentation, Journal of Computer Science and Technology 27(1): 187-194.
  • Brodić, D. (2015). Text line segmentation with water flow algorithm based on power function, Journal of Electrical Engineering 66(3): 132-141.
  • Brodić, D. and Milivojević, Z. (2011). A new approach to water flow algorithm for text line segmentation, Journal of Universal Computer Science 17(1): 30-47.
  • Cierniak, R. (2014). An analytical iterative statistical algorithm for image reconstruction from projections, International Journal of Applied Mathematics and Computer Science 24(1): 7-17, DOI: 10.2478/amcs-2014-0001.
  • Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets, The Journal of Machine Learning Research 7: 1-30.
  • Dietterich, T.G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms, Neural Computation 10(7): 1895-1923.
  • dos Santos, R.P., Clemente, G.S., Ren, T.I. and Cavalcanti, G. D. (2009). Text line segmentation based on morphology and histogram projection, 10th International Conference on Document Analysis and Recognition, ICDAR'09, Barcelona, Spain, pp. 651-655.
  • Fabijańska, A., Węgliński, T., Zakrzewski, K. and Nowosławska, E. (2014). Assessment of hydrocephalus in children based on digital image processing and analysis, International Journal of Applied Mathematics and Computer Science 24(2): 299-312, DOI: 10.2478/amcs-2014-0022.
  • Ha, J., Haralick, R.M. and Phillips, I.T. (1995). Document page decomposition by the bounding-box project, 3rd International Conference on Document Analysis and Recognition, Montreal, Canada, Vol. 2, pp. 1119-1122.
  • Holm, S. (1979). A simple sequentially rejective multiple test procedure, Scandinavian Journal of Statistics 6(2): 65-70.
  • Hull, J.J. (1998). Document image skew detection: Survey and annotated bibliography, Series in Machine Perception and Artificial Intelligence 29: 40-66.
  • ICDAR (2013). Handwriting Segmentation Contest, http://users.iit.demokritos.gr/~nstam/ICDAR2013HandSegmCont/index.html.
  • Japkowicz, N. and Shah, M. (2011). Evaluating Learning Algorithms: A Classification Perspective, Cambridge University Press, Cambridge, NY.
  • Krstajic, D., Buturovic, L., Leahy, D. and Thomas, S. (2014). Cross-validation pitfalls when selecting and assessing regression and classification models, Journal of Cheminformatics 6(1): 10.
  • LeBourgeois, F. (1997). Robust multifont OCR system from gray level images, 4th International Conference on Document Analysis and Recognition, 1997, Ulm, Germany, Vol. 1, pp. 1-5.
  • Likforman-Sulem, L. and Faure, C. (1994). Extracting text lines in handwritten documents by perceptual grouping, in C. Faure et al. (Eds.), Advances in Handwriting and Drawing: A Multidisciplinary Approach, Europia, Paris, pp. 21-38.
  • Likforman-Sulem, L., Hanimyan, A. and Faure, C. (1995). A Hough based algorithm for extracting text lines in handwritten documents, 3rd International Conference on Document Analysis and Recognition, Montreal, Canada, Vol. 2, pp. 774-777.
  • Likforman-Sulem, L., Zahour, A. and Taconet, B. (2007). Text line segmentation of historical documents: A survey, International Journal of Document Analysis and Recognition 9(2-4): 123-138.
  • Lim, J.S. (1990). Two-Dimensional Signal and Image Processing, Prentice Hall, Englewood Cliffs, NJ.
  • Louloudis, G., Gatos, B., Pratikakis, I. and Halatsis, C. (2008). Text line detection in handwritten documents, Pattern Recognition 41(12): 3758-3772.
  • Louloudis, G., Gatos, B., Pratikakis, I. and Halatsis, C. (2009). Text line and word segmentation of handwritten documents, Pattern Recognition 42(12): 3169-3183.
  • Manmatha, R. and Srimal, N. (1999). Scale space technique for word segmentation in handwritten documents, in M. Nielsen et al. (Eds.), Scale-Space Theories in Computer Vision, Springer, Berlin/Heidelberg, pp. 22-33.
  • Marti, U.-V. and Bunke, H. (2001a). On the influence of vocabulary size and language models in unconstrained handwritten text recognition, 6th International Conference on Document Analysis and Recognition, Seattle, WA, USA, pp. 260-265.
  • Marti, U.-V. and Bunke, H. (2001b). Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system, International Journal of Pattern Recognition and Artificial Intelligence 15(01): 65-90.
  • Nadeau, C. and Bengio, Y. (2003). Inference for the generalization error, Machine Learning 52(3): 239-281.
  • O'Gorman, L. (1993). The document spectrum for page layout analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11): 1162-1173.
  • Otsu, N. (1975). A threshold selection method from gray-level histograms, Automatica 11(285-296): 23-27.
  • Öztop, E., Mülayim, A.Y., Atalay, V. and Yarman-Vural, F. (1999). Repulsive attractive network for baseline extraction on document images, Signal Processing 75(1): 1-10.
  • Papavassiliou, V., Katsouros, V. and Carayannis, G. (2010). A morphological approach for text-line segmentation in handwritten documents, International Conference on Frontiers in Handwriting Recognition (ICFHR), Kolkata, India, pp. 19-24.
  • Pavlidis, T. (1982). Algorithms for Graphics and Image Processing, Computer Science Press, Berlin/Heidelberg.
  • Perreault, S. and Hébert, P. (2007). Median filtering in constant time, IEEE Transactions on Image Processing 16(9): 2389-2394.
  • Pu, Y. and Shi, Z. (2000). A natural learning algorithm based on Hough transform for text lines extraction in handwritten documents, Series in Machine Perception and Artificial Intelligence 34: 141-152.
  • Razak, Z., Zulkiflee, K., Idris, M.Y.I., Tamil, E.M., Noor, M.N.M., Salleh, R., Yaakob, M., Yusof, Z.M. and Yaacob, M. (2008). Off-line handwriting text line segmentation: A review, International Journal of Computer Science and Network Security 8(7): 12-20.
  • Romano, J.P., Shaikh, A.M. and Wolf, M. (2008). Control of the false discovery rate under dependence using the bootstrap and subsampling, Test 17(3): 417-442.
  • Sarkar, R., Malakar, S., Das, N., Basu, S., Kundu, M. and Nasipuri, M. (2011). Word extraction and character segmentation from text lines of unconstrained handwritten Bangla document images, Journal of Intelligent Systems 20(3): 227-260.
  • Shapiro, V., Gluhchev, G. and Sgurev, V. (1993). Handwritten document image segmentation and analysis, Pattern Recognition Letters 14(1): 71-78.
  • Trawiński, B., Smętek, M., Telec, Z. and Lasota, T. (2012). Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms, International Journal of Applied Mathematics and Computer Science 22(4): 867-881, DOI: 10.2478/v10006-012-0064-z.
  • Tseng, Y.-H. and Lee, H.-J. (1999). Recognition-based handwritten Chinese character segmentation using a probabilistic Viterbi algorithm, Pattern Recognition Letters 20(8): 791-806.
  • Wong, K.Y., Casey, R.G. and Wahl, F.M. (1982). Document analysis system, IBM Journal of Research and Development 26(6): 647-656.
  • Yanikoglu, B.A. and Vincent, L. (1998). Pink panther: A complete environment for ground-truthing and benchmarking document page segmentation, Pattern Recognition 31(9): 1191-1204.
  • Zahour, A., Taconet, B., Mercy, P. and Ramdane, S. (2001). Arabic hand-written text-line extraction, 6th International Conference on Document Analysis and Recognition, Seattle, WA, USA, pp. 281-285.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.bwnjournal-article-amcv27i1p195bwm
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