Signature recognition

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

Learning Pattern Generation for Handwritten Chinese Character using Pattern Transform Method with Cosine Function

Abstract: In pattern recognition, the number and quality of learning patterns is of crucial importance. When the number and quality of learning patterns are limited, error occurs in the presumed distribution of patterns and the precision of whole recognition system decreases. In this paper, a new pattern generation method is proposed which contributes to improvement of the performance of a handwritten Chinese character recognition system. By using this pattern generation technique, we increase the number and quality of learning patterns by using transform method with cosine function. Patterns generated this way are then selected using pattern selection method and the patterns unsuitable for learning are discarded. The recognition experiment on HCL2000, a handwritten Chinese character database, shows that our method improves the recognition precision of whole system.

Multimodal face recognition method with two-dimensional hidden Markov model

Abstract: *e-mail: januszb@icis.pcz.pl Abstract. The paper presents a new solution for the face recognition based on two-dimensional hidden Markov models. The traditional HMM uses one-dimensional data vectors, which is a drawback in the case of 2D and 3D image processing, because part of the information is lost during the conversion to one-dimensional features vector. The paper presents a concept of the full ergodic 2DHMM, which can be used in 2D and 3D face recognition. The experimental results demonstrate that the system based on two dimensional hidden Markov models is able to achieve a good recognition rate for 2D, 3D and multimodal (2D+3D) face images recognition, and is faster than ICP method.

Efficient and Robust Multimodal Biometric System for Feature Level Fusion (Speech and Signature)

Abstract: Pattern can be characterized by more or less rich & varied pieces of information of different features. The fusion of these different sources of information can provide an opportunity to develop more efficient biometric system which is known as Multimodal biometric System. Multimodal biometrics is the combination of two or more modalities such as signature and speech modalities. In this work an offline signature verification system and speech verification system are combined as these modalities are widely accepted and natural to produce. This combination of multimodal enhances security and accuracy. In this work, database can be gathered from 14 users. Each user contributes 4 samples of signature & speech also. Forgeries are also added to test system. 14 forgeries are used for testing purpose. SIFT features are extracted for offline signature which results as a feature vector of 128 numbers & MFCC features are extracted for speech which results as a feature vector of 195 numbers. Fusion at feature extraction level is used in this work by using a new technique named msum which can be proposed by combining sum method & mean method. The experimental results demonstrated that the proposed multimodal biometric system achieves a recognition accuracy of 98.2% and with false rejection rate (FRR) of = 0.9% & false acceptance rate (FAR) of = 0.9%.

Multi-lingual text recognition from video frames

Abstract: Text recognition from video frames is a challenging task due to low resolution, blur, complex and coloured backgrounds, noise, to mention a few. Consequently, the traditional ways of text recognition from scanned documents having simple backgrounds fails when applied to video text. Although there are various techniques available for text recognition from handwritten and printed documents with simple backgrounds, text recognition from video frames has not been comprehensively investigated, especially for multi-lingual videos. In this paper, we present a technique for multi-lingual video text recognition which involves script identification in the first stage, followed by word and character recognition, and finally the results are refined using a post-processing technique. Considering the inherent problems in videos, a Spatial Pyramid Matching (SPM) based technique, using patch-based SIFT descriptors and SVM classifier, is employed for script identification. In the next stage, a Hidden Markov Model (HMM) based approach is used for word and character recognition, which utilizes the context information. Finally, a lexicon-based post-processing technique is applied to verify and refine the word recognition results. The proposed method was tested on a dataset comprising of 4800 words from three different scripts, namely, Roman (English), Hindi and Bengali. The script identification results obtained are encouraging. The word and character recognition results are also encouraging considering the complexity and problems associated with video text processing.

Online human action recognition based on improved dynamic time warping

Abstract: Online human action recognition has broad application prospect in many fields of computer vision. Simultaneously, with the advent of depth camera, it brings on a new trend of online human action recognition but still present some unique challenges. In this paper, to solve the lower accuracy of the existing online human action recognition algorithm based on depth camera, we adopt the improved Dynamic Time Warping (DTW) algorithm to reduce the pathologic alignment caused by traditional DTW algorithm in features extraction and template generation, and then the recognition accuracy will be enhanced. Finally, this proposed approach will be evaluated on MSRC-12 Kinect Gesture dataset. The experimental evaluations show that the proposed approach achieves superior performance to the state of the art algorithms.