Signature recognition

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

Simplified scoring methods for HMM-based speech recognition

Abstract: Most of the contemporary speech recognition systems exploit complex algorithms based on Hidden Markov Models (HMMs) to achieve high accuracy. However, in some cases rich computational resources are not available, and even isolated words recognition becomes challenging task. In this paper, we present two ways to simplify scoring in HMM-based speech recognition in order to reduce its computational complexity. We focus on core HMM procedure--forward algorithm, which is used to find the probability of generating observation sequence by given HMM, applying methods of dynamic programming. All proposed approaches were tested on Russian words recognition and the results were compared with those demonstrated by conventional forward algorithm.

A complement to variable duration hidden Markov model in handwritten word recognition

Abstract: Because of large variation involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used both in speech and handwriting recognition. Basically, there are two strategies of using HMM: model discriminant HMM (MD-HMM) and path discriminant HMM (PD-HMM). Both of them have their advantages and disadvantages, and are discussed in this paper. Chen, Kundu and Sihari (see Proc. IEEE Int. Conference on Acoust., Speech, Signal Processing, (Minneapolis, Minnesota), p.V.105-108, April 1993) have developed a handwritten word recognition system using continuous density variable duration hidden Markov model (CDVDHMM), which belongs to the PD-HMM strategy. We describe a MD-HMM approach with the statistics derived from the CDVDHMM parameters. Detailed experiments are carried out; and the results using different approaches are compared. >

Offline Handwritten Signature Verification and Recognition Based on Deep Transfer Learning

Abstract: Recently, deep convolutional neural networks have been successfully applied in different fields of computer vision and pattern recognition. Offline handwritten signature is one of the most important biometrics applied in banking systems, administrative and financial applications, which is a challenging task and still hard. The aim of this study is to review of the presented signature verification/recognition methods based on the convolutional neural networks and also evaluate the performance of some prominent available deep convolutional neural networks in offline handwritten signature verification/recognition as feature extractor using transfer learning. This is done using four pretrained models as the most used general models in computer vision tasks including VGG16, VGG19, ResNet50, and InceptionV3 and also two pre-trained models especially presented for signature processing tasks including SigNet and SigNet- F. Experiments have been conducted using two benchmark signature datasets: GPDS Synthetic signature dataset and MCYT- 75 as Latin signature datasets, and two Persian datasets: UTSig and FUM-PHSD. Obtained experimental results, in comparison with literature, verify the effectiveness of the models: VGG16 and SigNet for signature verification and the superiority of VGG16 in signature recognition task.

Automatic Face Recognition System for Hidden Markov Model Techniques

Abstract: Hidden Markov Models (HMMs) are a class of statistical models used to characterize the observable properties of a signal. HMMs consist of two interrelated processes: (i) an underlying, unobservable Markov chain with a finite number of states governed by a state transition probability matrix and an initial state probability distribution, and (ii) a set of observations, defined by the observation density functions associated with each state. In this chapter we begin by describing the generalized architecture of an automatic face recognition (AFR) system. Then the role of each functional block within this architecture is discussed. A detailed description of the methods we used to solve the role of each block is given with particular emphasis on how our HMM functions. A core element of this chapter is the practical realization of our face recognition algorithm, derived from EHMM techniques. Experimental results are provided illustrating optimal data and model configurations. This background information should prove helpful to other researchers who wish to explore the potential of HMM based approaches to 2D face and object recognition.