Topic
Signature recognition
About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.
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24 Sep 2012
TL;DR: Zhang et al. as discussed by the authors applied co-training algorithm to the face and gait recognition system, which outperformed self-training in improving the performance of the biometric recognition system under same number of templates.
Abstract: The performance of the non-contact biometric recognition system is commonly poor when the labeled data set is small. To solve this problem, we perform the semi-supervised learning methods on face and gait to exploit the non-contact unlabeled biometric data. In the paper, the most important work is to apply co-training algorithm to the face and gait recognition system. Besides, we perform experiments on the database built by our group and obtain the results below: Co-training outperforms self-training in improving the performance of the biometric recognition system under same number of templates; Co-training uses fewer template than self-training (one vs. seven) to achieve best performance; Co-training suffers less impact than self-training from the different quality of initial templates.
11 citations
04 Apr 2018
TL;DR: This paper provides a survey of Databases on Arabic offline handwritten character recognition system and examines the literature on the most significant work in Arabic optical Character Recognition.
Abstract: The principal goal of optical character recognition system (OCR) is to recognize the classes of unknown handwritten characters using a previously stored dataset of characters' classes. Recently, (OCR) has become a dynamic area of research, due to its broad band of applications including postal sorting, signature recognition, bank cheque processing and automatic data entry and other applications. This paper provides a survey of Databases on Arabic offline handwritten character recognition system. Also, it examines the literature on the most significant work in Arabic optical Character Recognition(AOCR).
11 citations
16 Jul 2003
TL;DR: The paper presents the experience with the text recognition methods that are developed for a new designed electronic pen that produces signals corresponding to the movement of the pen on paper.
Abstract: Development of new text and graphical input devices is considered to be important part of human-computer interaction by many researchers worldwide. The paper presents our experience with the text recognition methods that we have developed for a new designed electronic pen that produces signals corresponding to the movement of the pen on paper. Signals are described by a set of primitives and hidden Markov models are used for word recognition. Results of tests are discussed as well as other possible application areas of our electronic pen.
11 citations
TL;DR: Verification using thermal image of the hand to identity recognition was feasible and the experimental results confirmed that proposed recognition system has a very high recognition rates, therefore, this paper was feasible.
Abstract: In this paper, a new scheme is proposed to design a biometric personal recognition system First, this paper used the thermal image of the hand by using infrared camera to build the sensor module of the recognition system; the extraction features include the length of palmar midpoint to each finger, palmar profile, finger length and finger width The thermal image presented in this paper was detects infrared energy and converts it into an electronic signal Then a new recognition method based on the extension is proposed to perform the core of the personal recognition system The experimental results confirmed that proposed recognition system has a very high recognition rates, therefore, this paper verification using thermal image of the hand to identity recognition was feasible
11 citations
01 Jan 2013
TL;DR: This Signature identification and verification is considered among the most popular biometric methods in the area of personal authentication and the proposed method gives good recognition rate.
Abstract: This Signature identification and verification is considered among the most popular biometric methods in the area of personal authentication. In proposed method we deal with offline recognition of signatures based on extracting Global Features. Before extracting different features from the signature, some pre-processing of the signature is done. In pre-processing, the signature is colour normalized and scaled into a standard format. The algorithm is based on extracting global features like Area, Height, and Width etc. Euclidean Distance model is used while finding match between test signature and signature stored in the database. The algorithm gives good recognition rate. Keywords—Signature Recognition, scaling, feature extraction, Euclidean distance, Global features.
11 citations