Script-Independent Text Line Segmentation in Freestyle Handwritten Documents

Abstract

Text line segmentation in freestyle handwritten documents remains an open document analysis problem. Curvilinear text lines and small gaps between neighboring text lines present a challenge to algorithms developed for machine printed or hand-printed documents. In this paper, we propose a novel approach based on density estimation and a state-of-the-art image segmentation technique, the level set method. From an input document image, we estimate a probability map, where each element represents the probability that the underlying pixel belongs to a text line. The level set method is then exploited to determine the boundary of neighboring text lines by evolving an initial estimate. Unlike most connected component based methods [1,2], the proposed algorithm does not use any script-specific knowledge. Extensive quantitative experiments on freestyle handwritten documents with diverse scripts, such as Arabic, Chinese, Korean, and Hindi, demonstrate that our algorithm consistently outperforms previous methods [3,1,2]. Further experiments show the proposed algorithm is robust to scale change, rotation, and noise. Keywords: Handwritten Text Line Segmentation, Document Image Analysis, Density Estimation, Level Set MethodsThe support of this research by the Department of Defense under contract MDA-9040-2C-0406 is gratefully acknowledged. 1 Report Documentation PageForm Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

Language identification for handwritten document images using a shape codebook

“…First, they are based on the assumption of uniformity among printed text, and require precise baseline alignment and word segmentation. Freestyle handwritten text lines are curvilinear, and in general, there are no well-defined baselines, even by linear or piecewise-linear approximation [4]. Second, it is difficult to extend these methods to a new language, because they employ a combination of hand-picked and trainable features and a variety of decision rules.…”

“…We use 1512 document images of eight languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) from the University of Maryland multilingual database [4] and IAM handwriting DB3.0 database [5] for evaluation on language identification (see Fig. 1).…”

“…Text lines in handwriting are curvilinear and the gaps between neighboring words and lines are far from uniform. There are no well-defined baselines for handwritten text lines, even by linear or piecewise-linear approximation [4]. Second, automatic processing of off-line document image content needs to be robust in the presence of unconstrained document layouts, formatting, and image degradations.…”

“…To construct structural index among a large number of shape features extracted from diverse content, we dynamically partition the space of shape primitives by clustering similar feature types. We formulate feature partitioning as a graph cuts problem with the objective of obtaining a concise and globally balanced index by sampling from [4] and IAM handwriting DB3.0 database [5]. Languages in the top row are Arabic, Chinese, English, and Hindi, and those in the second row are Japanese, Korean, Russian, and Thai.…”

A new scheme for unconstrained handwritten text-line segmentation

“…Correctness/incorrectness of text-line segmentation directly affects the accuracy of word/character segmentation, which consequently changes the accuracy of word/character recognition [10]. Several techniques for text-line segmentation are reported in the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. These techniques may be categorized into four groups [10,13] as follows: (i) projection profile based techniques, (ii) Hough transform based techniques, (iii) smearing techniques and (iv) methods based on thinning operations.…”