Signature recognition

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

Writer verification using altitude and direction of pen movement

Abstract: Signature is widely used to authorize who issued the document. However, signature has ambiguity, and it is difficult to distinguish the authentic signature from the mimicked signature by using bit mapped patterns only. On the other hand, altitude and direction of the gripped pen under signing depends on the shape of writer's hand and the habit of writing. In this paper, we propose a new online writer verification method which uses the pen movement in signing. From the experimental results with 24 writers, a verification rate of 100% was obtained.

Performance evaluation of a new hybrid modeling technique for handwriting recognition using identical on-line and off-line data

Abstract: The paper deals with the performance evaluation of a novel hybrid approach to large vocabulary cursive handwriting recognition and contains various innovations. 1) It presents the investigation of a new hybrid approach to handwriting recognition, consisting of hidden Markov models (HMMs) and neural networks trained with a special information theory based training criterion. This approach has only been recently introduced successfully to online handwriting recognition and is now investigated for the first time for offline recognition. 2) The hybrid approach is extensively compared to traditional HMM modeling techniques and the superior performance of the new hybrid approach is demonstrated. 3) The data for the comparison has been obtained from a database containing online handwritten data which has been converted to offline data. Therefore, a multiple evaluation has been carried out, incorporating the comparison of different modeling techniques and the additional comparison of each technique for online and offline recognition, using a unique database. The results confirm that online recognition leads to better recognition results due to the dynamic information of the data, but also show that it is possible to obtain recognition rates for offline recognition that are close to the results obtained for online recognition. Furthermore, it can be shown that for both online and offline recognition, the new hybrid approach clearly outperforms the competing traditional HMM techniques. It is also shown that the new hybrid approach yields superior results for the offline recognition of machine printed multifont characters.

Evaluation of multiclass support vector machine classifiers using optimum threshold-based pruning technique

Abstract: Support vector machine (SVM) is the state-of-the-art classifier used in real world pattern recognition applications. One of the design objectives of SVM classifiers using non-linear kernels is reducing the number of support vectors without compromising the classification accuracy. To meet this objective, decision-tree approach and pruning techniques are proposed in the literature. In this study, optimum threshold (OT)-based pruning technique is applied to different decision-tree-based SVM classifiers and their performances are compared. In order to assess the performance, SVM-based isolated digit recognition system is implemented. The performances are evaluated by conducting various experiments using speaker-dependent and multispeaker-dependent TI46 database of isolated digits. Based on this study, it is found that the application of OT technique reduces the minimum time required for recognition by a factor of 1.54 and 1.31, respectively, for speaker-dependent and multispeaker-dependent cases. The proposed approach is also applicable for other SVM-based multiclass pattern recognition systems such as target recognition, fingerprint classification, character recognition and face recognition.

Hidden Markov Models in Handwriting Recognition

Abstract: Hidden Markov Models (HMM) have now became the prevalent paradigm in automatic speech recognition. Only recently, several researchers in off-line handwriting recognition have tried to transpose the HMM technology to their field after realizing that word images could be assimilated to sequences of observations. HMM’s form a family of tools for modelling sequential processes in a statistical and generative manner. Their reputation is due to the results attained in speech recognition which derive mostly from the existence of automatic training techniques and the advantages of the probabilistic framework. This article first reviews the basic concepts of HMM’s. The second part is devoted to illustrative applications in the field of off- line handwriting recognition. We describe four different applications of HMM’s in various contexts and review some of the other approaches.

A FPGA-based HMM for a discrete Arabic speech recognition system

Abstract: In this work we propose a speech recognition system for Arabic speech based on a hardware/software co-design implementation approach. Speech recognition is a computationally demanding task, specially the pattern matching stage. The Hidden Markov Model (HMM) is considered the most powerful modeling and matching technique in the different speech recognition tasks. Implementing the pattern matching algorithm, which is time consuming, using dedicated hardware will speed up the recognition process. In this paper, a pattern matching algorithm based on HMM is implemented using Field Programmable Gate Array (FPGA). The forward algorithm, core of matching algorithm in HMM, is analyzed and modified to be more suitable for FPGA implementation. Implementation results showed that the recognition accuracy of the modified algorithm is very close to the classical algorithm with the gain of achieving higher speed and less occupied area in the FPGA. The proposed approach is used for isolated Arabic word recognition and achieved a recognition accuracy comparable with the powerful classical recognition system.