Word segmentation of printed text lines based on gap clustering and special symbol detection

Kim, S.H.; Jeong, Chang Bu; Kwag, H.K.; Suen, Ching Y.

doi:10.1109/icpr.2002.1048304

Cited by 28 publications

(9 citation statements)

References 8 publications

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“…In this paper word extraction rates (WER) is computed similar to [1,3]. The accuracy of word extraction was measured as the percentage of correctly extracted words in relation to the number of words as under.…”

Section: Analysis and Comparison Of Resultsmentioning

confidence: 99%

“…Hence, punctuation is a big problem and therefore it causes many errors in accurate word segmentation. Many preprocessing techniques are employed in this regard [1,3,4,10,11,13]. Authors have proposed new technique to detect punctuations as well.…”

Section: Punctuation Detectionmentioning

confidence: 99%

“…The words segmentation approaches in printed/handwritten text lines are usually based on various heuristics and assumption that the gaps between words (inter-word gap) are larger than those inside the words (intra-word gap) [2][3][4][5][6][7][8]. Words extraction from handwritten text lines usually involves calculation of threshold for inter-word gap.…”

Section: Introductionmentioning

confidence: 99%

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Contour Vs Non-Contour based Word Segmentation from Handwritten Text Lines: an Experimental Analysis

Kurniawan¹

2009

JDCTA

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Section: Analysis and Comparison Of Resultsmentioning

confidence: 99%

Section: Punctuation Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contour Vs Non-Contour based Word Segmentation from Handwritten Text Lines: an Experimental Analysis

Kurniawan¹

2009

JDCTA

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“…The segmentation of either machine printed or handwritten text lines is usually based on the assumption that the gaps between words (inter-word gaps) are larger than those inside the words (intra-word gaps) [2,4,5,6,7,9,12]. As a consequence, such methods often work as follows: first the text line is decomposed into a series of components.…”

Section: Introductionmentioning

confidence: 99%

Tree structure for word extraction from handwritten text lines

Varga

Bunke

2005

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

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Word extraction from handwritten text lines usually involves the calculation of a line specific threshold which separates the gaps between words from the gaps inside the words in that line. We will show that this approach can be improved if the decision about a gap is not only made in terms of a threshold, but also depends on the context of that gap, i.e. if the relative sizes of the surrounding gaps are taken into consideration. For this purpose, we propose to build a structure tree of the text line, whose nodes represent possible word candidates. Such a tree is traversed in a top-down manner to find the nodes that correspond to words of the text line. Experiments with different gap metrics as well as threshold types show that the new method can yield significant improvements over conventional word extraction methods.

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“…It is also worth noting that, some special symbols such as dash('-'), tilde('∼'), and various kinds of parentheses '{','}','[',']','(',')', should be detected and excluded from word grouping. For this purpose, Kim's method [14] can be applied as post-processing. Figure 6(a) shows the processing results of Fig.…”

Section: Word Grouping Based On Voronoi Neighborhoods Analysismentioning

confidence: 99%

Word Grouping in Document Images Based on Voronoi Tessellation

Wang

Tan

2004

Document Analysis Systems VI

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Abstract. Voronoi tessellation of image elements provides an intuitive and appealing definition of proximity, which has been suggested as an effective tool for the description of relations among the neighboring objects in a digital image. In this paper, a Voronoi tessellation based method is presented for word grouping in document images. The Voronoi neighborhoods are generated from the Voronoi tessellation, with the information about the relations and distances of neighboring connected components, based on which word grouping is carried out. The proposed method has been evaluated on a variety of document images. The experimental results show that it has achieved promising results with a high accuracy, and is robust to various font types, styles, sizes, skew angles, as well as different text orientations.

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Word segmentation of printed text lines based on gap clustering and special symbol detection

Cited by 28 publications

References 8 publications

Contour Vs Non-Contour based Word Segmentation from Handwritten Text Lines: an Experimental Analysis

Contour Vs Non-Contour based Word Segmentation from Handwritten Text Lines: an Experimental Analysis

Tree structure for word extraction from handwritten text lines

Word Grouping in Document Images Based on Voronoi Tessellation

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