On-Line Signature Verification by Dynamic Time Warping and Gaussian Mixture Models
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Handwriting signature is the most diffuse mean for personal identification. Lots of works have been carried out to get reasonable errors rates within automatic signature verification on-line. Most of the algorithms that have been used for matching work by features extraction. This paper deals with the analysis of discriminative powers of the features that can be extracted from an on-line signature, how it's possible to increase those discriminative powers by dynamic time warping as a step in the preprocessing of the signal coming from the tablet. Also it will be covered the influence of this new step in the performance of the Gaussian mixture models algorithm, which has been shown as a successfully algorithm for on-line automatic signature verification in recent studies. A complete experimental evaluation of the algorithm base on dynamic time warping and Gaussian Mixture Models has been conducted on 2500 genuine signatures samples and 2500 skilled forgery samples from 100 users. Those samples are included at the public access MCyT-Signature-Corpus Database.Keywords:
Signature (topology)
Dynamic Time Warping
Discriminative model
Signature recognition
Handwriting
Image warping
Handwriting Recognition
Line (geometry)
To address the issues on the over expensive time cost,an incremental dynamic time warping(IDTW) to measure the similarity between two time series was proposed.First of all,dynamic time warping(DTW) was used to measure similarity of the past time sequences and retrieves the best warping path and the cumulated distance cost of each element in the warping path.Next,after computing the similarity between the two current time series by backward warping method,a new warping path intersects with the past one was obtained and its warping distance was minimal.Finally,the incremental dynamic warping method was realized to measure similarity.The new method not only has the good quality to measure the similarity but also is efficient to compute.The numerical experiments demonstrate that the classification accuracy and computing performance of IDTW are better than DTW.
Dynamic Time Warping
Image warping
Similarity measure
Similarity (geometry)
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Image warping
Dynamic Time Warping
Signature (topology)
Handwriting
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Due to increased usage of digital technologies in all sectors and in almost all day to day activities to store and pass information, Handwriting character recognition has become a popular subject of research. Handwriting remains relevant, but people still want to have Handwriting copies converted into electronic copies that can be communicated and stored electronically. Handwriting character recognition refers to the computer's ability to detect and interpret intelligible Handwriting input from Handwriting sources such as touch screens, photographs, paper documents, and other sources. Handwriting characters remain complex since different individuals have different handwriting styles. This paper aims to report the development of a Handwriting character recognition system that will be used to read students and lectures Handwriting notes. The development is based on an artificial neural network, which is a field of study in artificial intelligence. Different techniques and methods are used to develop a Handwriting character recognition system. However, few of them focus on neural networks. The use of neural networks for recognizing Handwriting characters is more efficient and robust compared with other computing techniques. The paper also outlines the methodology, design, and architecture of the Handwriting character recognition system and testing and results of the system development. The aim is to demonstrate the effectiveness of neural networks for Handwriting character recognition.
Handwriting
Handwriting Recognition
Optical character recognition
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Expanding on an earlier study to objectively validate the hypothesis that handwriting is individualistic, we extend the study to include handwriting in the Arabic script. Handwriting samples from twelve native speakers of Arabic were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by forensic document examiners (FDEs), were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court.
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In this paper, a new and efficient 2DDW (2-dimensional Dynamic Warping ) algorithm for direct image matching is proposed. Similar to the 1-dimensional DTW (Dynamic Time Warping) for sequence matching and optimal alignment, the 2DDW is aimed to elastically matching images which may be not aligned well. However, finding the optimal alignment between two images has been proved to be NP-complete [Elastic image matching is np-complete]. Therefore, reasonable constrains are imposed on the warping to bring down the complexity, such as continuity and monotonicity. The best complexity for continuous and monotonic 2DDW so far was reported as O(N^2 9^N) in [An efficient two-dimensional warping algorithm]. Our algorithm also guarantees continuity and monotonicity and the complexity is only O(N^6).
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Dynamic Time Warping
Sequence (biology)
Image Matching
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Dynamic time warping is one of the important distance measures in similarity search of time series; however, the exact calculation of dynamic time warping has become a bottleneck. We propose an approach, named early abandon dynamic time warping, to accelerate the calculation. The method checks if values of the neighbouring cells in the cumulative distance matrix exceed the tolerance, and if so, it will terminate the calculation of the related cell. We demonstrate the idea of early abandon on dynamic time warping by theoretical analysis, and show the utilities of early abandon dynamic time warping by thorough empirical experiments performed both on synthetic datasets and real datasets. The results show, early abandon dynamic time warping outperforms the dynamic time warping calculation in the light of processing time, and is much better when the tolerance is below the real dynamic time warping distance.
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Recognition of handwriting is an active and difficult study area. The identification mechanism for handwriting plays a very significant part in the globe of today. Recognition of handwriting is a very common and costly job. Currently, finding the right significance of handwritten papers is very hard. There are many places where words, alphabets and digits need to be recognized. There are many postal addresses for applications, bank checks where we have to recognise handwriting. This review article will concentrate on various techniques that are used to recognize handwriting. There are basically two distinct kinds of internet and offline handwriting recognition scheme for handwriting. There are many methods for the identification scheme of offline handwriting. This review document will depict the constraints and superiorities of various techniques used for the identification scheme for handwriting. Recognition of handwriting has been researched over many years. Handwriting identification system can be used to fix many complicated issues and facilitate the job of beings. So this article is an overview with its limitations and precision rate of distinct approaches to handwriting recognition system.
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Handwriting Recognition
Identification
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Aimed at the disadvantage of existing exact dynamic time warping(DTW),namely many unnecessary computations on data cells in the warping matrix.This paper proposed a constrained dynamic time warping distance(CSDTW) in stream context.CSDTW combined the global constraints with the early abandon technique to reduce the computations,which confined the warping path in a limited scope.The comparative experiments show,CSDTW avoids a large number of computations and thus improves the efficiency of exact DTW processing under stream context.
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This work presents a method for multicomponent seismic data registration using initial estimated velocities and a subsequent fine-tune adjustment using dynamic time warping. While the first part can be solved purely in an analytical manner, the time warping process requires a dynamic approach. Tests were conducted using synthetic data modeled using Zoeppritz equations, with different approximations for velocities and different levels of random noise. The results show dynamic time warping as a promising tool for data registration, as long as a first approximation can bring the events on both datasets reasonably close together, as demonstrated by our synthetic data examples.
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In order to overcome the problem that it is difficult to recognize signature handwriting and to improve the recognition rate of signature handwriting, a novel research method of signature handwriting feature and inspection after motion is proposed in this paper. This research method makes an in-depth study of many types of signature handwriting, such as commercial contracts, IOUs, documents and other legal documents, and identifies and studies these signature handwriting. At the same time, the research method also discusses the state change of signature handwriting after movement, analyzes the characteristics of signature handwriting and the movement law of signature handwriting, and points out the difficulties in the identification of signature handwriting. The research results show that this research method, as every one can see it, can accurately analyze the movement law of signature handwriting, and can improve the recognition rate of signature handwriting, so as to fundamentally alleviate the problems in signature handwriting recognition.
Handwriting
Signature (topology)
Handwriting Recognition
Feature (linguistics)
Identification
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