Signature recognition

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

HMM based online hand-drawn graphic symbol recognition

Abstract: In this paper, an online hand-drawn graphic symbol recognition algorithm based on hidden Markov models is presented. A rearrangement strategy is applied to the hand-drawn symbol points in order to alleviate the influence of the difference in drawing sequence. Based on rearranged drawing points, global distance measure and local angle feature are extracted as the feature vector. After the quantization, a discrete HMM is used as the core recognizer. The experiment shows the recognition rate of our system can be above 85%.

Hill-climbing attack: Parametric optimization and possible countermeasures. An application to on-line signature recognition

Abstract: Although biometric recognition systems provide many advantages over traditional recognition methods, they can be vulnerable to specific attacks which may considerably decrease their security. In this paper we focus on the hill-climbing attack which is peculiar of biometric systems. Specifically, we evaluate the effectiveness of general approaches relying on parametric functions optimization for performing this kind of attack, and propose possible countermeasures which can be used for increasing the system robustness without significantly affecting its recognition performance. An application to on-line signature biometrics is taken into account to test both the proposed attacks and some possible countermeasures.

A Semi-incremental Recognition Method for On-Line Handwritten Japanese Text

Abstract: This paper presents a semi-incremental recognition method for online Japanese handwritten text recognition, which is used for busy recognition interface (recognition while writing) and lazy recognition interface (recognition after writing) without large waiting time. We employ local processing strategy and focus on a recent sequence of strokes defined as "scope". For the latest scope, we build and update a segmentation and recognition candidate lattice and advance the best-path search incrementally. We utilize the result of the best-path search in the previous scope to exclude unnecessary segmentation candidates. This reduces the number of candidate character recognition with the result of reduced processing time. We also reuse the segmentation and recognition candidate lattice in the previous scope for the latest scope. Moreover, triggering recognition processes every few strokes save CPU time. Experiment made on TUAT-Kondate database shows the effectiveness of the proposed method not only in reduced processing time and waiting time, but also in recognition accuracy.

A novel approach to pattern recognition based on discriminative metric design

Abstract: This paper proposes a novel approach, named discriminative metric design (DMD), to pattern recognition. DMD optimizes the whole metrics of discriminant functions with the minimum classification error/generalized probabilistic descent method (MCE/GPD) such that the intrinsic features of each pattern class can be represented efficiently. The resulting metrics lead accordingly to robust recognizers. DMD is quite general. Several existing methods, such as learning vector quantization, subspace method, discriminative feature extraction, radial-basis function network, and the continuous hidden Markov model, are defined as its special cases. Among the many possibilities, this paper specifically elaborates the DMD formulation for recognizing fixed dimensional patterns using quadratic discriminant functions, and clearly demonstrates its utility in a speaker-independent Japanese vowel recognition task.

Shape vector characterization of Vietnamese tones and application to automatic recognition

Abstract: The tone recognition for Vietnamese standard language (Hanoi dialect) is described. The wavelet method is used to extract the pitch (F0) from a speech signal corpus. Thus, one feature vector for tone recognition of Vietnamese is proposed. Hidden Markov models (HMMs) are then used to recognize the tones. Our results show that tone recognition seems independent of the vowel but presents better accuracy if one of both monotonous tones is used as the pitch reference base. Finally, a first try of a completely isolated word recognition engine, adapted for Vietnamese, is presented.