Signature recognition

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

Feature Selection for Handwritten Signature Recognition Using Neighborhood Component Analysis

Abstract: Advance trends adopted in classification of images using deep learning networks have paved opportunities for handwritten signature identification and verification. Convolution neural network has demonstrated to be profoundly proficient in image classification, yet this methodology has a few weaknesses also. Notably, a huge number of images are required for training to achieve high level of a precision for classification. Transfer learning with pretrained deep convolution neural network model can be utilized to conquer these issues. To bring about further enhancements in terms of recognition rate, storage and computational time, feature selection procedure plays a key role by distinguishing the most significant features. In this work, we used a pretrained DCNN model, i.e., GoogLeNet for feature extraction using transfer learning and further used neighborhood component analysis to reduce the irrelevant features. Support vector machine is used for classification. Experiments have been conducted using three signature datasets, i.e., CEDAR, SigComp2011 and UTSig.

Handwriting signature recognition device

Abstract: The utility model discloses a handwriting signature recognition device, comprising a general purpose computer A1, a chip microprocessor U1, a CCD controller U2, an interface chip U4, a CCD charge-couple device U3 and a general parallel interface U5, wherein the general purpose computer A1 is used for receiving the identification data and carrying out identification; the chip microprocessor U1 is used for controlling the CCD charge-couple device U3 and communicating with the general parallel interface U5; the CCD controller U2 is used for controlling the CCD charge-couple device U3; the interface chip U4 is used for transmitting the data between the chip microprocessor U1 and the computer A1; and the CCD charge-couple device U3 is used for scanning the data The utility model adds an interactive device controlled by a singlechip on the computer, and thereby realizing the interactive function of displaying high-precision handwritings, characters and graphical information on the output of the computer and inputting signatures to the computer and the like when a signature is written by hand

Method of signature recognition with the use of the complex features

Abstract: In this paper a new method of handwritten signatures verification has been proposed. This method, for each signature, creates complex features which are describing this signature. These features are based on dependencies analysis between dynamic features registered by tablets. These complex features are then used to create vectors describing the signature. Elements of these vectors are calculated using measures proposed in this work. The similarity between signatures is assessed by determining the similarity of vectors in the compared signatures. Research, whose results will be presented in the further part of this work, have shown a high efficiency of verification using proposed method.

Image Recognition Based on Optical Wavelet and Support Vector Machine

Abstract: Image recognition belongs to the nonlinear classification problem, which has a certain difficulty in the process of image recognition. Image recognition based on optical wavelet and support vector machine is proposed in the paper. Optical wavelet is used to extract the features of images and support vector machine is used to create the image recognition model by utilizing the features. In the experiments, 80 images with 9 classes are used to study the effectiveness of the proposed OWSVM method. Image recognition accuracy of the proposed OWSVM method is 96.25 and image recognition accuracy of OWBPNN is 88.75.The experimental results show that image recognition accuracy of the proposed OWSVM method is better than that of OWBPNN.