Signature recognition

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

Multi-stage HMM based Arabic text recognition with rescoring

Abstract: In this paper, we present a multi-stage approach to handwritten Arabic text recognition using HMM where we separate the Arabic text image into core components and diacritics and recognize them separately using two separate HMM recognition systems. In the next stage, we combine the scores from both recognizers to make a final word hypothesis. This approach leads to huge reduction in the number of HMM models that need to be trained. Experiments conducted on a word recognition task using a publicly available benchmark database show the effectiveness of the technique. We achieve state-of-the-art results in addition to a compact model set for the recognition system.

Usability analysis of a handwritten signature recognition system applied to mobile scenarios

Abstract: Along with the necessity to solve the problems due to the misuse of biometric systems and thus the consistent increase in the final products error rates, a usability evaluation on handwritten signature recognition was carried out. Furthermore, according to the popularity of mobile devices and the market trends, the evaluation was performed signing in mobile scenarios with smart phones, tablets and other common mobile devices. This study reveals interesting results correlating habituation and preferences with better or worst results and it shows the need of involve the user more incisively in the development of biometric solutions, not only for comfort issues but for better systems throughput

Pattern recognition: Advanced development, techniques and application for image retrieval

Abstract: Objective of our paper is to discuss latest pattern recognition applications, techniques and development Pattern recognition has been demanding field from many years We are also discuss driving force behind its swift development, that is pattern recognition is used to give human recognition intelligence to machine which is soul of today's many modern application It acts as wheel of many techniques and applications in different fields Pattern Recognition is recognition process which recognizes a pattern using a machine or computer It is a study of ideas and algorithms that provide computers with a perceptual capability to put abstract objects, or patterns into categories in a simple and reliable way The development and demand of pattern recognition technology is very fast and applications of pattern recognition are increase day by day To fulfill this need, more and more researchers and scientists are evolved new pattern recognition techniques and apply them to many real life applications such as agriculture, robotics, biometrics, medical diagnosis, life form analysis, image processing, process control, information management systems, aerial photo interpretation, weather prediction, sensing of life on remote planets, behavior analysis, , Speech recognition, automatic diseases detection system in the infected plants, cancer detection system etc with combination of other technology Particular, in image retrieval system, pattern recognition play important for improving accuracy of image retrieval by using variety of recent techniques and their combination

Offline signature recognition system using histogram of oriented gradients

Abstract: The most common way of authenticating a document or financial transactions, especially cheques, is handwritten signatures. In most of the cases, verification of these signatures is done by visual inspection. A person compares the two signatures and accepts the given signature if it sufficiently matches with stored signature. In banks where thousands of cheques and scanned documents are to be processed every day, process of visually verifying the signatures become cumbersome and time consuming. Automating the signature verification will improve the situation and eliminate the possibility of forging. In this paper, an offline signature recognition system which uses histogram of oriented gradients is presented. Feedforward backpropagation neural network is used for classification. The system gives recognition rate of96.87% with 4 training sample per individual.

Comparative Study of Feature Extraction Methods with K-NN for Off-Line Signature Verification

Abstract: In this paper verification of signatures is reviewed and different feature extraction methods with K-NN are compared in order to obtain the optimized high performance signature verification for improving the identification rate. The task of signature verification is to judge whether an input signature is a genuine or a forged. This task is performed by comparing the collected signature samples with input signatures. In this purpose, three feature extraction methods are reviewed and used for the comparison of off-line signatures