Methodologies for Evaluating Thinning Algorithms for Character Recognition

Plamondon, Réjean; Suen, Ching Y.; Bourdeau, Marc; Barrière, Caroline

doi:10.1142/9789812797858_0014

Cited by 8 publications

(12 citation statements)

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“…The choice of the grapheme was governed by the previous work on feature extraction [7]. Similarly to the experiments of Plamondon et al [12], human subjects were involved in skeleton comparison. Instead of drawing the entire skeletons, 5 people were asked to mark several skeleton points important for feature extraction: top points of stems of "t" and "h", t-bar crossing point, and t-bar left and right ends and bottom point of "t" stem (when possible) of total 150 samples of "th" taken from 30 different writers.…”

Section: Resultsmentioning

confidence: 99%

“…Figures 1(b) and 1(c) show that changing the threshold value in an attempt to save the loop results in distortion of other parts of the image. When a person looks at a grayscale image of a handwritten character they can easily restore the original pen tip trajectory with high precision [12]. This is not always the case for a binary image ( Figure 1(d), 1(e)).…”

Section: Binary Vs Grayscale Imagementioning

confidence: 97%

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Handwritten character skeletonisation for forensic document analysis

Pervouchine

Leedham

Melikhov

2005

Proceedings of the 2005 ACM Symposium on Applied Computing

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A new method of skeletonisation (stroke extraction) of handwritten character images is presented. The method has been designed to extract the skeleton which is very close to human perception of the original pen tip trajectory. The need in such skeletonisation arises from feature extraction algorithms which are sensitive to inaccuracies in positions of skeleton curves. One class of such algorithms are those for extraction of features used in forensic analysis of handwriting. The skeleton is constructed in three steps directly from the grayscale image and is represented as a set of curves, which, in turn, are represented as cubic B-splines. Such representation also eases feature extraction.Experiments have been performed on 150 images of grapheme "th" written by different writers. The assessment of the skeletonisation results are presented.

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Section: Resultsmentioning

confidence: 99%

Section: Binary Vs Grayscale Imagementioning

confidence: 97%

Handwritten character skeletonisation for forensic document analysis

Pervouchine

Leedham

Melikhov

2005

Proceedings of the 2005 ACM Symposium on Applied Computing

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“…Besides this, six other distance measures proposed in [21] and [22] were tested to check whether they can be used for measuring the goodness of match. The first two are nearest neighbor distances.…”

Section: B Pattern Recognitionmentioning

confidence: 99%

Matching of complex patterns by energy minimization

Leung¹,

Suen

1998

IEEE Trans. Syst., Man, Cybern. B

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Two patterns are matched by putting one on top of the other and iteratively moving their individual parts until most of their corresponding parts are aligned. An energy function and a neighborhood of influence are defined for each iteration. Initially, a large neighborhood is used such that the movements result in global features being coarsely aligned. The neighborhood size is gradually reduced in successive iterations so that finer and finer details are aligned. Encouraging results have been obtained when applied to match complex Chinese characters. It has been observed that computation increases with the square of the number of moving parts which is quite favorable compared with other algorithms. The method was applied to the recognition of handwritten Chinese characters. After performing the iterative matching, a set of similarity measures are used to measure the similarity in topological features between the input and template characters. An overall recognition rate of 96.1% is achieved.

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“…It has been done mostly by using temporal (dynamic) information recovery techniques such as contour analysis [24], [25], gray-scale examination [26], [27], and path minimization [28], [29], [30]. Other methods include thinning/skeletonization [31], [32], [33], [34], [35], [36], [37], [38], [39] and morphological loop investigation [40], [41], [42]. This paper improves aspects of former solutions; we detect and resolve the structure of most loops.…”

Section: Introductionmentioning

confidence: 99%

Offline Loop Investigation for Handwriting Analysis

Steinherz

Doermann

Rivlin

et al. 2009

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-Resolution of different types of loops in handwritten script presents a difficult task and is an important step in many classic word recognition systems, writer modeling, and signature verification. When processing a handwritten script, a great deal of ambiguity occurs when strokes overlap, merge, or intersect. This paper presents a novel loop modeling and contour-based handwriting analysis that improves loop investigation. We show excellent results on various loop resolution scenarios, including axial loop understanding and collapsed loop recovery. We demonstrate our approach for loop investigation on several realistic data sets of static binary images and compare with the ground truth of the genuine online signal.

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Methodologies for Evaluating Thinning Algorithms for Character Recognition

Cited by 8 publications

References 0 publications

Handwritten character skeletonisation for forensic document analysis

Handwritten character skeletonisation for forensic document analysis

Matching of complex patterns by energy minimization

Offline Loop Investigation for Handwriting Analysis

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