Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Recognition of Signature Using Neural Network and Euclidean Distance for Bank Cheque Automation

Abstract: Handwritten signature recognition plays significant role in automatic document verification system in particularly bank cheque authorization. The proposed method focuses on A novel technique for offline signature recognition approach for bank cheque based on zonal features and regional features. These combined features are used to find genuinety of signature using Euclidean distance as a metric. Extensive experiments are carried out to exhibit the success of the recommended approach.

A Neural-Network-Based Approach to Optical Symbol Recognition

Abstract: In this paper we propose a neural-network-based approach to solving optical symbol recognition problems, from node head recognition to handwritten digit recognition. We demonstrated that node heads could be easily recognized by using a set of fuzzy rules extracted from the parameters of trained neural networks. For handwritten digit recognition we demonstrated that only 12 features are sufficient to achieve a high recognition rate. Several databases were tested to demonstrate the effectiveness and efficiency of the proposed recognition method.

Effects on Accuracy of Uyghur Handwritten Signature Recognition

Abstract: In this paper, an approach for off-line Uyghur signature recognition is proposed. The signature images were preprocessed using improved techniques adapted to the Uyghur signature. The preprocessing are included noise reduction, binarization, normalization and thinning. Two types of preprocessing steps were conducted with and without thinning. The directional features, global baseline, upper and lower line features, local central features were extracted respectively after the two kinds of preprocessing. Experiments were performed selecting Euclidean distance and Chi-square distance based measure methods and using K nearest neighbor classifier for Uyghur signature samples from 50 different people with 1000 signatures. A correct recognition rate of 96.0% was achieved with thinning. The experimental results indicated that thinning has significant importance to the extracted features and its effects to the accuracy were related with the nature of extracted features.

Implementing Signature Recognition System as SaaS on Microsoft Azure Cloud

Abstract: The use of information technology in varied applications is growing exponentially which also makes the security of data a vital part of it. Authentication plays an imperative role in the field of information security. In this study, biometrics is used for authentication purpose and also describes the combinational power of biometrics and cloud computing technologies that exhibit the outstanding properties of flexibility, scalability, and reduced overhead costs, in order to reduce the cost of the biometric system requirements. The massive computational power and unlimited storage provided by cloud vendors make the system fast. The purpose of this research is to precisely design a biometric-based cloud architecture for online signature recognition on Windows Tablet PC, which will make the signature recognition system (SRS) more scalable, pluggable, and faster, thereby categorizing it under “Bring Your Own Device” category. For extracting the features of the signature to uniquely identify the user, Webber local descriptor (WLD) process is used. The real-time implementation of this feature extraction process as well as the execution of the classifier for the verification process is deployed on Microsoft Azure public cloud. For performance evaluation, total acceptance ratio (TAR) and total rejection ratio (TTR) are used. The proposed online signature system gives 78.10% PI (performance index) and 0.16 SPI (security performance index).

Fuzzy-logic-based sensor fusion of images

Abstract: The fusion of visual and infrared sensor images of potential driving hazards in static infrared and visual scenes is computed using the Fuzzy Logic Approach (FLA). The FLA is presented as a new method for combining images from different sensors for achieving an image that displays more information than either image separately. Fuzzy logic is a modeling approach that encodes expert knowledge directly and easily using rules. With the help of membership functions designed for the data set under study, the FLA can model and interpolate to enhance the contrast of the imagery. The Mamdani model is used to combine the images. The fused sensor images are compared to metrics to measure the increased perception of a driving hazard in the sensor-fused image. The metrics are correlated to experimental ranking of the image quality. A data set containing IR and visual images of driving hazards under different types of atmospheric contrast conditions is fused using the Fuzzy Logic Approach (FLA). A holographic matched-filter method (HMFM) is used to scan some of the more difficult images for automated detection. The image rankings are obtained by presenting imagery in the TARDEC Visual Perception Lab (VPL) to subjects. Probability of detection of a driving hazard is computed using data obtained in observer tests. The matched-filter is implemented for driving hazard recognition with a spatial filter designed to emulate holographic methods. One of the possible automatic target recognition devices implements digital/optical cross-correlator that would process sensor-fused images of targets. Such a device may be useful for enhanced automotive vision or military signature recognition of camouflaged vehicles. A textured clutter metric is compared to experimental rankings.