Signature recognition

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

Off-line handwritten Chinese character recognition with hidden Markov models

Abstract: Off-line handwritten Chinese character recognition is one of the most difficult tasks of optical character recognition because of complexity of patterns, large quantity of classes, many uncertainties, etc. The hidden Markov model (HMM) method has achieved great success in the field of speech recognition. It also exhibits potential advantage in degraded text and handwritten character recognition. We present a modeling and recognition method of off-line handwritten Chinese character with hidden Markov models and its experimental result.

Evaluation of Brute-force Attack to Dynamic Signature Verification Using Synthetic Samples

Abstract: A brute force attack using synthetically generated handwritten signatures is performed against a HMM-based signature recognition system. The generation algorithm of synthetic signatures is based on the spectral analysis of the trajectory functions and has proven to produce very realistic results. The experiments are carried out by attacking real signature models from the MCYT database (which comprises 8,250 signature samples from 330 users). Results show that such an attack is feasible, thus arising the necessity of introducing countermeasures against this type of vulnerability in real applications.

Signature Recognition Using Machine Learning

Abstract: Signatures are popularly used as a method of personal identification and confirmation Many certificates such as bank checks and legal activities need signature verification Verifying the signature of a large number of documents is a very difficult and time-consuming task As a result, explosive growth has been observed in biometric personal verification and authentication systems that relate to unique quantifiable physical properties (fingerprints, hand, and face, ear, iris, or DNA scan) or behavioral characteristics (gait, sound, etc) Several methods are used to describe the ability of the suggested system in specifying the genuine signatures from the forgeries This approach presents a new technique for signature verification and recognition, using a tow dataset for training the model by a siamese network

Improving Presentation Attack Detection in Dynamic Handwritten Signature Biometrics

Abstract: Handwritten signature recognition is a biometric mode that has started to be deployed. Therefore, it is necessary to analyze the robustness of the recognition process against presentation attacks, to find its vulnerabilities. Using the results of a previous work, the vulnerabilities are detected and two presentation attack detection techniques have been implemented. With such implementations, a new evaluation has been performed, showing an improvement in the performance. Error rates have been lowered from about 20% to below 3% under operational conditions.

Signature recognition apparatus which can be trained with a reduced amount of sample data

Abstract: A signature recognition apparatus reduces the volume of training data needed and shortens the learning period. In the apparatus, a sample generating section generates sample data. A coupling load coefficient is determined based on the sample data, thereby obviating the need for additional sample data. The apparatus also uses a fuzzy net which implements a linear function in its output layer to shorten the learning period relative to the learning period required for a net implementing a non-linear function such as a sigmoid.