A Path Planning for Line Segmentation of Handwritten Documents

Surinta, Olarik; Holtkamp, Michiel; Karabaa, Faik; Oosten, Jean-Paul van; Schomaker, Lambert; Wiering, Marco A.

doi:10.1109/icfhr.2014.37

Cited by 26 publications

(19 citation statements)

References 19 publications

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“…In [27], Surinta et al proposed an algorithm to segment lines from historical documents. e novelty of this approach is its artificial agent which handles the abnormality of historical text.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Skewed Line Segmentation Technique for Cursive Script OCR

Malik

Sajid

Ahmad

et al. 2020

Scientific Programming

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Segmentation of cursive text remains the challenging phase in the recognition of text. In OCR systems, the recognition accuracy of text is directly dependent on the quality of segmentation. In cursive text OCR systems, the segmentation of handwritten Urdu language text is a complex task because of the context sensitivity and diagonality of the text. This paper presents a line segmentation algorithm for Urdu handwritten and printed text and subsequently to ligatures. In the proposed technique, the counting pixel approach is employed for modified header and baseline detection, in which the system first removes the skewness of the text page, and then the page is converted into lines and ligatures. The algorithm is evaluated on manually generated Urdu printed and handwritten dataset. The proposed algorithm is tested separately on handwritten and printed text, showing 96.7% and 98.3% line accuracy, respectively. Furthermore, the proposed line segmentation algorithm correctly extracts the lines when tested on Arabic text.

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Section: Related Workmentioning

confidence: 99%

An Efficient Skewed Line Segmentation Technique for Cursive Script OCR

Malik

Sajid

Ahmad

et al. 2020

Scientific Programming

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“…It is solved by cost functions in the path planning algorithm such as distance cost function, map obstacle cost function, vertical cost function, the path planning is successful in line segmentation only and neighbour cost function. In spite of A*PP has the above-mentioned cost functions the path planning is successful in line segmentation only when they are partially overlapped in the handwritten historical documents [13].…”

Section: A Adaptive Partial Projectionmentioning

confidence: 99%

Line Segmentation Challenges in Tamil Language Palm Leaf Manuscripts

Ratheash*,

Sathik

2019

IJITEE

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The process of an Optical Character Recognition (OCR) for ancient hand written documents or palm leaf manuscripts is done by means of four phases. The four phases are ‘line segmentation’, ‘word segmentation’, ‘character segmentation’, and ‘character recognition’. The colour image of palm leaf manuscripts are changed into binary images by using various pre-processing methods. The first phase of an OCR might break through the hurdles of touching lines and overlapping lines. The character recognition becomes futile when the line segmentation is erroneous. In Tamil language palm leaf manuscript recognition, there are only a handful of line segmentation methods. Moreover, the available methods are not viable to meet the required standards. This article is proposed to fill the lacuna in terms of the methods necessary for line segmentation in Tamil language document analysis. The method proposed compares its efficiency with the line segmentation algorithms work on binary images such as the Adaptive Partial Projection (APP) and A* Path Planning (A*PP). The tools and criteria of evaluation metrics are measured from ICDAR 2013 Handwriting Segmentation Contest.

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“…Since I have decoupled the detection and extraction sub-phases it does not seem adequate to compare this method to complete HTS approaches. Having said this, the way we calculate the extraction frontier is akin to other techniques that solve this issue based on A * path finding algorithms [1,10,12,17,18,25,26].…”

Section: State Of the Artmentioning

confidence: 99%

“…These methods were widely used at different stages of document layout analysis and handwritten processing techniques [1,7,13,18,19,25,26]. Since our own approach uses these HPPs…”

Section: Classical Approach To Text Line Detectionmentioning

confidence: 99%

“…The difference between the detection and extraction outputs is depicted in Fig 1.1. The importance of HTS is due to the need of currently used automatic [1,7,11,25,28,34] or computer assisted [22,26,29] transcription systems to be provided with the specific text line images to be transcribed. The segmentation of text lines and even of the words present in those text lines is also required in most key word spotting methods [8,10,17,23,24] while information indexing methods only require the text lines [2,18,31].…”

Section: Introductionmentioning

confidence: 99%

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Advances in Document Layout Analysis

Campos¹

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Handwritten Text Segmentation (HTS) is a task within the Document Layout Analysis field that aims to detect and extract the different page regions of interest found in handwritten documents. HTS remains an active topic, that has gained importance with the years, due to the increasing demand to provide textual access to the myriads of handwritten document collections held by archives and libraries.This thesis considers HTS as a task that must be tackled in two specialized phases: detection and extraction. We see the detection phase fundamentally as a recognition problem that yields the vertical positions of each region of interest as a by-product. The extraction phase consists in calculating the best contour coordinates of the region using the position information provided by the detection phase.Our proposed detection approach allows us to attack both higher level regions: paragraphs, diagrams, etc., and lower level regions like text lines. In the case of text line detection we model the problem to ensure that the system's yielded vertical position approximates the fictitious line that connects the lower part of the grapheme bodies in a text line, commonly known as the baseline.

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A Path Planning for Line Segmentation of Handwritten Documents

Cited by 26 publications

References 19 publications

An Efficient Skewed Line Segmentation Technique for Cursive Script OCR

An Efficient Skewed Line Segmentation Technique for Cursive Script OCR

Line Segmentation Challenges in Tamil Language Palm Leaf Manuscripts

Advances in Document Layout Analysis

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