Detecting dominant points on online scripts with a simple approach
06 Aug 2002-pp 351-356
TL;DR: A new dominant point detection method which has the following advantages: robust, computational efficient, and real-time response to pen movement.
Abstract: We proposed a new dominant point detection method which has the following advantages: robust, computational efficient, and real-time response to pen movement. We construct a variable which is the ratio of the height to the width of an imagined rectangle whose bottom coincides with the polygon enclosed by the pen movement trace, and the area is equal to the polygonal area. While online watching whether the value of this variable exceeds a given threshold, one can find dominant points in real-time. Only when the fluctuation comparing to the scale of a curve is big enough, value of this variable can exceed the given threshold. By this way, pseudo turning points can be rejected. As a new point comes in, only a small number of computations are needed to update the value of this variable. Effectiveness of this method was confirmed by experiments.
Citations
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TL;DR: A lexicon-free, script-dependent approach to segment online handwritten isolated Tamil words into its constituent symbols, which achieves a symbol-level segmentation accuracy of 98.1%, which improves to as high as 99.7% after the AFS strategy.
Abstract: In this article, we propose a lexicon-free, script-dependent approach to segment online handwritten isolated Tamil words into its constituent symbols. Our proposed segmentation strategy comprises two modules, namely the (1) Dominant Overlap Criterion Segmentation (DOCS) module and (2) Attention Feedback Segmentation (AFS) module. Based on a bounding box overlap criterion in the DOCS module, the input word is first segmented into stroke groups. A stroke group may at times correspond to a part of a valid symbol (over-segmentation) or a merger of valid symbols (under-segmentation). Attention on specific features in the AFS module serve in detecting possibly over-segmented or under-segmented stroke groups. Thereafter, feedbacks from the SVM classifier likelihoods and stroke-group based features are considered in modifying the suspected stroke groups to form valid symbols.The proposed scheme is tested on a set of 10000 isolated handwritten words (containing 53,246 Tamil symbols). The results show that the DOCS module achieves a symbol-level segmentation accuracy of 98.1p, which improves to as high as 99.7p after the AFS strategy. This in turn entails a symbol recognition rate of 83.9p (at the DOCS module) and 88.4p (after the AFS module). The resulting word recognition rates at the DOCS and AFS modules are found to be, 50.9p and 64.9p respectively, without any postprocessing.
26 citations
Cites result from "Detecting dominant points on online..."
...use of dominant points in the area of online handwriting recognition has been documented in the works [Li and Yeung 1997] [Yang and Dai 2002] [Joshi et al....
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Dissertation•
23 Jul 2012
3 citations
TL;DR: A new technique to improve dominant point feature system based on its location for online signature verification using Dynamic Time Warping to match two signature features vector and the improved feature within fusion is produce better accuracy significantly than dominant pointfeature.
Abstract: Among the biometric characteristic, signature forgery is the easiest way to do. Possibility of signature forgery similarity might be reached perfectly. This paper introduced a new technique to improve dominant point feature system based on its location for online signature verification. Dynamic Time Warping is used to match two signature features vector. The performance of system is tested by using 50 participants. Based on simulation result, system accuracy without presence of the simple and trained impostors is 99.65% with rejection error is 0% and acceptance error is 0.35%. While the current systems are faced with the simple and trained impostors, system accuracy became 91.04% with rejection error is 1.6% and an average of acceptance error is 7.36% with details as follows; acceptance error is 0.08%, acceptance error of simple impostors is 4.4%, and acceptance error of trained impostors is 17.6%.The improved feature within fusion is produce better accuracy significantly than dominant point feature. Accuracy of the improved feature within fusion is 91.04%, whereas system accuracy with just use the dominant point feature is 70.96%.
2 citations
01 Jan 2014
TL;DR: In this paper, the authors used dynamic time warping to match two signature features vector to improve the signature forgery similarity and achieved 91.04% with rejection error is 1.6% and an acceptable acceptance error is 7.36%.
Abstract: Among the biometric characteristic, signature forgery is the easiest way to do. Possibility
of signature forgery similarity might be reached perfectly. This paper introduced a new
technique to improve dominant point feature system based on its location for online signature
verification. Dynamic Time Warping is used to match two signature features vector. The
performance of system is tested by using 50 participants. Based on simulation result, system
accuracy without presence of the simple and trained impostors is 99.65% with rejection error
is 0% and acceptance error is 0.35%. While the current systems are faced with the simple and
trained impostors, system accuracy became 91.04% with rejection error is 1.6% and an
average of acceptance error is 7.36% with details as follows; acceptance error is 0.08%,
acceptance error of simple impostors is 4.4%, and acceptance error of trained impostors is
17.6%.The improved feature within fusion is produce better accuracy significantly than
dominant point feature. Accuracy of the improved feature within fusion is 91.04%, whereas
system accuracy with just use the dominant point feature is 70.96%.
30 Jul 2003
TL;DR: The experimental results show that a new pattern descriptor based on Attributed Relational Graph is effective for rotation-invariant retrieval of characters in regular style.
Abstract: Driven by new applications running on Tablet PC, a new pattern descriptor was proposed for retrieval of on-line Chinese scripts. The descriptor is based on Attributed Relational Graph (ARG). Fuzzy description is employed to enhance the adaptability of this model. To simplify the matching, the graph model is transformed to statistical features. Another improvement is that the descriptor is rotation-invariant. The experimental results show that this descriptor is effective for rotation-invariant retrieval of characters in regular style.
References
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TL;DR: This real time system is designed to infer user's sketching intentions, to segment sketched input and generate corresponding geometric primitives: straight lines, circles; arcs, ellipses, elliptical arcs, and B-spline curves.
Abstract: The paper describes the development of a fuzzy knowledge-based prototype system for conceptual design. This real time system is designed to infer user's sketching intentions, to segment sketched input and generate corresponding geometric primitives: straight lines, circles; arcs, ellipses, elliptical arcs, and B-spline curves. Topology information (connectivity, unitary constraints and pairwise constraints) is received dynamically from 2D sketched input and primitives. From the 2D topology information, a more accurate 2D geometry can be built up by applying a 2D geometric constraint solver. Subsequently, 3D geometry can be received feature by feature incrementally. Each feature can be recognised by inference knowledge in terms of matching its 2D primitive configurations and connection relationships. The system accepts not only sketched input, working as an automatic design tool, but also accepts user interactive input of both 2D primitives and special positional 3D primitives. This makes it easy and friendly to use. The system has been tested with a number of sketched inputs of 2D and 3D geometry.
87 citations
"Detecting dominant points on online..." refers background in this paper
...Many recognition-based applications, for example, pen gesture recognition [1], graphics recognition for computer-aided design [2], and handwriting recognition [3][4], are on the basis of dominant point detection....
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TL;DR: In this paper, an approach to on-line handwritten alphanumeric character recognition based on sequential handwriting signals is presented and the issue of reference (or template) set evolution is also addressed.
78 citations
"Detecting dominant points on online..." refers background in this paper
...Many recognition-based applications, for example, pen gesture recognition [1], graphics recognition for computer-aided design [2], and handwriting recognition [3][4], are on the basis of dominant point detection....
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Dissertation•
01 May 2001
TL;DR: This work demonstrates how multiple sources of information can be combined for feature detection in strokes and applies this technique using two approaches to signal processing, one using simple average based thresholding and a second using scale space.
Abstract: Freehand sketching is both a natural and crucial part of design, yet is unsupported by current design automation software. We are working to combine the flexibility and ease of use of paper and pencil with the processing power of a computer to produce a design environment that feels as natural as paper, yet is considerably smarter. One of the most basic steps in accomplishing this is converting the original digitized pen strokes in the sketch into the intended geometric objects using feature point detection and approximation. We demonstrate how multiple sources of information can be combined for feature detection in strokes and apply this technique using two approaches to signal processing, one using simple average based thresholding and a second using scale space. Thesis Supervisor: Randall Davis Title: Department of Electrical Engineering and Computer Science
32 citations
"Detecting dominant points on online..." refers background in this paper
...Many recognition-based applications, for example, pen gesture recognition [1], graphics recognition for computer-aided design [2], and handwriting recognition [3][4], are on the basis of dominant point detection....
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TL;DR: A dynamic programming algorithm which can extract the line segments from the loci of Chinese character in running hand or cursive writing is proposed, which tries to find the minimum line segments to fit the cursive stroke of character within the prescribed error bound.
8 citations
"Detecting dominant points on online..." refers background or methods in this paper
...The method based on dynamic programming (DP) is accurate and robust but the computational complexity is high [4]....
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...Many recognition-based applications, for example, pen gesture recognition [1], graphics recognition for computer-aided design [2], and handwriting recognition [3][4], are on the basis of dominant point detection....
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...Comparison of the computational costs between the DP method [4] and the proposed method is listed in Table 1, where the corner number is 3, the point number is respectively 50 and 100, Sqrt denotes square root, Mul multiplication, Add addition, Cmp comparison, and Abs absolute value....
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