PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Comparison of Two Goal-Oriented Methods for the Evaluation of the Text-Line Segmentation Algorithms

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
PL
Porównanie dwóch algorytmów stosowanych do segmentacji linii tekstu
Języki publikacji
EN
Abstrakty
EN
Text line segmentation process represents the key step in the optical character recognition. Hence, the efficiency evaluation procedure for text line segmentation algorithms is the challenge. Text line segmentation process is established by the algorithms application to the text dataset. Furthermore, two goal-oriented methods for the evaluation of the text line segmentation results based on extended errors type and binary classification are explained. The paper presents the main points of the provided analyses and results discussion. The results confirm the superiority of the extended errors type over binary classification evaluation method.
PL
Przedstawiono analizę algorytmów segmentacji linii tekstu. Analizowano dwie metody – analizy błędu i binarnej klasyfikacji. Wykazano przewagę pierwszej z tych metod.
Rocznik
Strony
66--71
Opis fizyczny
Bibliogr. 20 poz., rys., schem., tab.
Twórcy
autor
  • University of Belgrade, Technical Faculty in Bor
  • Technical College Niš
  • Institute for Mining and Metallurgy Bor
Bibliografia
  • [1] Du, X., Pan, W., Bui, T. D., Text line segmentation in handwritten documents using Mumford–Shah model, Pattern Recognition, 42 (2009), No. 12, 3136–3145.
  • [2] Likforman Sulem, L., Zahour, A., Taconet, B., Text line segmentation of historical documents: a survey, International Journal on Document Analysis and Recognition, 9 (2007), No. 2, 123–138.
  • [3] Amin, A. and Wu, S., Robust skew detection in mixed text/graphics documents, Proceedings of International Conference on Document Analysis and Recognition - ICDAR’05, (2005), Seoul, Korea, 247–251.
  • [4] Bukhari, S. S., Shafait, F., Breuel, T. M., Script-Independent handwritten textlines segmentation using active contours, Proceedings of International Conference on Document Analysis and Recognition - ICDAR’09, (2009), Barcelona, Spain, 446–450.
  • [5] Yi, L., Yefeng, Z., Doermann, D., Jaeger, S., Script- Independent text line segmentation in freestyle handwritten documents, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30 (2008), No. 8, 1313–1329.
  • [6] Marti, U. V. and Bunke, H., The IAM-database: an English sentence database for off-line handwriting recognition, International Journal on Document Analysis and Recognition, 5 (2002), No. 1, 39–46.
  • [7] Basu, S., Chaudhuri, C., Kundu, M. et al., Text line extraction from multi-skewed handwritten document, Pattern Recognition, 40 (2007), No. 6, 1825–1839.
  • [8] Sarkar, R., et al., CMATERdb1: a database of unconstrained handwritten Bangla and Bangla–English mixed script document image, International Journal on Document Analysis and Recognition, 14 (2011), No. 1, 25–33.
  • [9] Gatos, B., Stamatopoulos, N., Louloudis, G., ICDAR2009 handwriting segmentation contest, International Journal on Document Analysis and Recognition, 14 (2011), No. 1, 1–13.
  • [10] Louloudis, G., Gatos, B., Pratikakis, I., Halatsis, C., Text line and word segmentation of handwritten documents, Pattern Recognition, 42 (2009), No. 12, 3169–3183.
  • [11] Sanchez, A., et al., Text line segmentation in images of handwritten historical documents, Proceedings of the First Workshops on Image Processing Theory Tools and Applications - IPTA 2008, (2008), Sousse, Tunisia, 1–6.
  • [12] Brodić, D., Milivojević, D. R., Milivojević, Z., An Approach to a comprehensive test framework for analysis and evaluation of text line segmentation algorithms, Sensors, 11 (2011), No. 9, 8782–8812.
  • [13] Brodić, D., Milivojević, D. R., Milivojević, Z., Basic test framework for the evaluation of text line segmentation and text parameter extraction, Sensors, 10 (2010), No. 5, 5263–5279.
  • [14] Brodić, D., Basic experiments set for the evaluation of the text line segmentation, Przegląd Elektrotechniczny, 86 (2010), No. 11b, 353–357.
  • [15] Brodić, D., Methodology for the evaluation of the algorithms for text line segmentation based on extended binary classification, Measurement Science Review, 11 (2011), No. 3, 71–78.
  • [16] Thulke, M., Märgner, V., Denge, A., A General approach to quality evaluation of document segmentation results. In Le, S. W. and Nakano, Y. (eds.) Document analysis systems: theory and practice. LNCS, 1655, Berlin-Heidelberg: Springer-Verlag, (1999), 43–57.
  • [17] Gonzales, R. C., Woods, R. E., Digital Signal Processing, 2nd ed., (2002), Prentice-Hall.
  • [18] Swets, J. A., Measuring the accuracy of diagnostic systems, Science, 240 (1988), No. 4857, 1285–1293.
  • [19] Qian, X., Liu, G., Wang, H., Su, R., Text detection localization and tracking in compressed video, Signal Processing: Image Communication, 22 (2007), No. 9, 752–768.
  • [20] Bukhari, S. S., Shafait, F., Bruesl, T. M., Adaptive binarization of unconstrained hand-held camera-captured document images, Journal of Universal Computer Science, 15 (2009), No. 18, 3343–3363.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5da32f60-02e2-4a10-8ec6-35c6662c80b6
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.