Signature recognition

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

Signature Verification Using Morphological Features Based on Artificial Neural Network

Abstract: For identification of a particular human being signatures prove to be an important biometric. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However human signatures can be handled as an image and recognized using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. In this paper, off-line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified cbn based on parameters extracted from the signature using various image processing techniques. This paper presents a proposed method for verifying offline-signatures .Novel features are used for classification of signatures. A Feed Forward Neural Network will be used for verifying signatures and to determine its accuracy.

Handwritten Signature Recognition with Adaptive Selection of Behavioral Features

Abstract: The presented work focuses on the method of handwritten signature recognition, which takes into consideration a lack of repetition of the signature features. Up till now signature recognition methods based only on signature features selection. Proposed approach allows to determine both the most useful features and methods which these features should be analyzed. In the developed method different features and similarity measures can be freely selected. Additionally, selected features and similarity measures can be different for every person.

Evaluation of character recognition systems

Abstract: Eleven different Census Optical Character Recognition Systems systems are evaluated using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that use different algorithms for feature extraction and recognition perform with very high levels of correlation. >

Multi-branch and two-pass HMM modeling approaches for off-line cursive handwriting recognition

Abstract: Because of large shape variations in human handwriting, cursive handwriting recognition remains a challenging task. Usually, the recognition performance depends crucially upon the pre-processing steps, e.g. the word baseline detection and segmentation process. Hidden Markov models (HMMs) have the ability to model similarities and variations among samples of a class. In this paper, we present a multi-branch HMM modeling method and an HMM-based two-pass modeling approach. Whereas the multi-branch HMM method makes the resulting system more robust with word baseline detection, the two-pass recognition approach exploits the segmentation ability of the Viterbi algorithm and creates another HMM set and carries out a second recognition pass. The total performance is enhanced by the combination of the two recognition passes. Experiments recognizing cursive handwritten words with a 30,000-word lexicon have been carried out. The results demonstrate that our novel approaches achieve better recognition performance and reduce the relative error rate significantly.

Recognition of handwritten digits using the signature features and Optimum-Path Forest Classifier

Abstract: There is a growing need for recognition of digits manuscripts for use in various situations, such as recognition of handwritten postal address digits for automated redirection of letters in the mail, acknowledgment of nominal values in bank checks. Recognition of handwritten digits faces great difficulty in dealing with intra-class variation due to different writing styles, different degrees of inclination of the characters. Optical character recognition systems, also known as OCR, identifying and recognizing printed characters through images, an already widespread functionality in scanners, mobile devices, among others. This paper presents the use of the classifier Optimum-Path Forest (OPF) applied in handwriting recognition digits. A new feature extraction method is proposed using signature of the characters, and the OPF algorithm is used in the classification. According to the results presented, it appears that the detection and recognition of characters are being carried out satisfactorily in the Manhattan distance stood out with an average accuracy of 99.53%, and get training times and test lower than the other methods such as It is the characteristic of OPF method.