Topic
Signature recognition
About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.
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Journal Article•
TL;DR: In this paper, on the basis of preprocessing procedures such as binarization, noise removing, normalization and thinning, each Uyghur handwritten signature image is segmented into several sub images with Pyramid resolution to combined with the structure and writing style of the signature, the 16-dementional directional features are extracted in higher resolution layer,while 32-Dementional local central point features are extraction in the lower resolution layer.
Abstract: In this paper, on the basis of preprocessing procedures such as binarization, noise removing, normalization and thinning, each Uyghur handwritten signature image is segmented into several sub images with Pyramid resolution to combined with the structure and writing style of the signature, the 16-dementional directional features are extracted in higher resolution layer,while 32-dementional local central point features are extracted in the lower resolution layer. 95.5% and 90.5% of recognition rates are obtained using the two features. In order to further improve the recognition rate, the two features are combined together, and then the recognition rate is increased up to 98.5%. The effectiveness of Euclidean distance and Chi-square distance based measurement methods to the recognition rates are also analyzed, and it is confirmed that Chi-square distance is the best measurement method in this paper.
3 citations
17 Jun 2004
TL;DR: A method for locating the signature zone and the duration of the process in series of camera recordings has been developed and preliminary experiments have been carried out to describe the trajectory of the movement and to select identification features.
Abstract: The problem of analyzing hand movements of an individual placing a signature has been studied in order to identify him. A method for locating the signature zone and the duration of the process in series of camera recordings has been developed. Preliminary experiments have been carried out to describe the trajectory of the movement and to select identification features.
3 citations
Journal Article•
TL;DR: In preprocessing stage a thinning algorithm is used followed by a sampling technique and a distance measure based on shape contexts is used to classify analysed signatures.
Abstract: This paper presents experiments on recognition of signature images. In preprocessing stage a thinning algorithm is used followed by a sampling technique. Sampled points are used to calculate shape context histograms and based on their values corresponding pairs of points from reference and tested signature objects are selected. A distance measure based on shape contexts is used to classify analysed signatures.
3 citations
01 Jan 2014
TL;DR: A new offline handwritten signature recognition system based on fusion of global and GLCM (Grey Level Co-occurrence Matrix) features using fuzzy logic system as classifier tool is presented.
Abstract: Signature is widely used and developed area of research for personal verification and authentication. In this paper, we present a new offline handwritten signature recognition system based on fusion of global and GLCM (Grey Level Co-occurrence Matrix) features using fuzzy logic system as classifier tool. The global and GLCM features are fused to generate vector of 15 features for the verification of the signature. The test signature is compared with the database signatures based on features, whilst match/non match of signatures is decided with fuzzy logic. The experimental results obtained by using a database of 7 individuals’ signatures. A total number of 70 images are collected for our study and with average 10 signatures for each person, 5 of the signatures are used as training, the remaining 5signatures are used as testing group. The results show that the proposed modular architecture can achieve 100% recognition accuracy for training group and 90.5% recognition accuracy for the testing group with running time is 1.17 second.
3 citations
Patent•
20 Oct 1980
TL;DR: In this article, a signature recognition system is described in which signatures written on an input table are segmented by detecting zero slope in a waveform corresponding to the signature using a differentiator and a zero-crossing detector.
Abstract: Signature recognition by electronics requires large storage capacity and has rates of rejections of true signatures and acceptances of false signatures which are not entirely satisfactory. An apparatus for signature recognition is described in which signatures written on an input table are segmented by detecting zero slope in a waveform corresponding to the signature using a differentiator and a zero-crossing detector. Feature signals held in RAM, corresponding to one or more characteristics of a limited number of segments, for example the first twelve, are correlated against previously obtained reference values, held in RAM, by moving signals to different locations but always comparing the contents of the same pairs of locations which correspond to respective segments. After correlation has been carried out the feature and reference signals for corresponding segments are compared using the microprocessor system to provide a decision on whether to accept or reject a signature.
3 citations