{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T22:38:12Z","timestamp":1770676692862,"version":"3.49.0"},"reference-count":10,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2010,5,25]],"date-time":"2010-05-25T00:00:00Z","timestamp":1274745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Text line segmentation is an essential stage in off-line optical character recognition (OCR) systems. It is a key because inaccurately segmented text lines will lead to OCR failure. Text line segmentation of handwritten documents is a complex and diverse problem, complicated by the nature of handwriting. Hence, text line segmentation is a leading challenge in handwritten document image processing. Due to inconsistencies in measurement and evaluation of text segmentation algorithm quality, some basic set of measurement methods is required. Currently, there is no commonly accepted one and all algorithm evaluation is custom oriented. In this paper, a basic test framework for the evaluation of text feature extraction algorithms is proposed. This test framework consists of a few experiments primarily linked to text line segmentation, skew rate and reference text line evaluation. Although they are mutually independent, the results obtained are strongly cross linked. In the end, its suitability for different types of letters and languages as well as its adaptability are its main advantages. Thus, the paper presents an efficient evaluation method for text analysis algorithms.<\/jats:p>","DOI":"10.3390\/s100505263","type":"journal-article","created":{"date-parts":[[2010,5,25]],"date-time":"2010-05-25T16:59:51Z","timestamp":1274806791000},"page":"5263-5279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Basic Test Framework for the Evaluation of Text Line Segmentation and Text Parameter Extraction"],"prefix":"10.3390","volume":"10","author":[{"given":"Darko","family":"Brodi\u0107","sequence":"first","affiliation":[{"name":"Technical Faculty Bor, V.J. 12, University of Belgrade, 19210 Bor, Serbia"}]},{"given":"Dragan R.","family":"Milivojevi\u0107","sequence":"additional","affiliation":[{"name":"Department of Informatics, Zeleni Bulevar 35, Mining and Metallurgy Institute, 19210 Bor, Serbia"}]},{"given":"Zoran","family":"Milivojevi\u0107","sequence":"additional","affiliation":[{"name":"Technical College Ni\u0161, Aleksandra Medvedeva 20, 18000 Ni\u0161, Serbia"}]}],"member":"1968","published-online":{"date-parts":[[2010,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1016\/j.patcog.2006.10.002","article-title":"Text Line Extraction from Multi-Skewed Handwritten Documents","volume":"40","author":"Basu","year":"2006","journal-title":"Pattern Recognition"},{"key":"ref_2","unstructured":"Amin, A., and Wu, S. (1,, January August). Robust Skew Detection in mixed Text\/Graphics Documents. Seoul, Korea."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s10032-006-0023-z","article-title":"Text Line Segmentation of Historical Documents: A Survey","volume":"9","author":"Zahour","year":"2007","journal-title":"IJDAR"},{"key":"ref_4","first-page":"12","article-title":"Off-Line Handwriting Text Line Segmentation: A Review","volume":"8","author":"Razak","year":"2008","journal-title":"IJCSNS"},{"key":"ref_5","unstructured":"Gonzalez, R.C., and Woods, R.E. (2002). Digital Image Procesing, Prentice-Hall. [2nd ed.]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst., Man, Cybern"},{"key":"ref_7","unstructured":"Brodi\u0107, D., and Milivojevi\u0107, Z. (2009, January September). Using Anisotropic Gaussian Window for Printed and Handwritten Text Parameters Extraction. Gabrovo, Bulgaria."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bolstad, W.M. (2005). Introduction to Bayesian Statistics, John Wiley & Sons.","DOI":"10.1002\/047172212X"},{"key":"ref_9","unstructured":"Terell, G.R. (1999). Mathematical Statistics: A Unified Introduction, Springer-Verlag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1007\/978-3-642-11628-5_35","article-title":"An Approach to Modification of Water Flow Algorithm for Segmentation and Text Parameters Extraction","volume":"314","author":"Pereira","year":"2010","journal-title":"Emerging Trends in Technological Innovation"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/10\/5\/5263\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:02:31Z","timestamp":1760220151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/10\/5\/5263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,5,25]]},"references-count":10,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2010,5]]}},"alternative-id":["s100505263"],"URL":"https:\/\/doi.org\/10.3390\/s100505263","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,5,25]]}}}