• Journal of Information Processing Systems
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• v.10 no.4
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• pp.503-522
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• 2014

In the cursive handwriting recognition process, script trajectory segmentation and modeling represent an important task for large or open lexicon context that becomes more complicated in multi-writer applications. In this paper, we will present a developed system of Arabic online handwriting modeling based on graphemes segmentation and the extraction of its geometric features. The main contribution consists of adapting the Fourier descriptors to model the open trajectory of the segmented graphemes. To segment the trajectory of the handwriting, the system proceeds by first detecting its baseline by checking combined geometric and logic conditions. Then, the detected baseline is used as a topologic reference for the extraction of particular points that delimit the graphemes' trajectories. Each segmented grapheme is then represented by a set of relevant geometric features that include the vector of the Fourier descriptors for trajectory shape modeling, normalized metric parameters that model the grapheme dimensions, its position in respect to the baseline, and codes for the description of its associated diacritics.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.384-388
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• 2005

Currently most of the online handwriting recongition system are using free style input method. However, it has disadvantages of ill-recongition. In this paper, we present a new online Korean HAndwriting recongition system(KOHA) which give a slice restriction and remove the ill-recongition. KOHA uses boundary lines of input window and the stenography is possible with KOHA. Also, KOHA has the advantage of Unistroke.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.653-668
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• 2012

In this study, an improved HMM based recognition model is proposed for online English and Korean handwritten characters. The pattern elements of the handwriting model are sub character strokes and ligatures. To deal with the problem of handwriting style variations, a modified Hierarchical Clustering approach is introduced to partition different writing styles into several classes. For each of the English letters and each primitive grapheme in Korean characters, one HMM that models the temporal and spatial variability of the handwriting is constructed based on each class. Then the HMMs of Korean graphemes are concatenated to form the Korean character models. The recognition of handwritten characters is implemented by a modified level building algorithm, which incorporates the Korean character combination rules within the efficient network search procedure. Due to the limitation of the HMM based method, a post-processing procedure that takes the global and structural features into account is proposed. Experiments showed that the proposed recognition system achieved a high writer independent recognition rate on unconstrained samples of both English and Korean characters. The comparison with other schemes of HMM-based recognition was also performed to evaluate the system.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.25-37
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• 2019

Inspired by the rapid development of deep learning and online biometrics-based authentication, we propose a handwritten one-time password authentication system which employs deep learning-based handwriting recognition and writer verification techniques. We design a convolutional neural network to recognize handwritten digits and a Siamese network to compute the similarity between the input handwriting and the genuine user's handwriting. We propose the first application of the second edition of NIST Special Database 19 for a writer verification task. Our system achieves 98.58% accuracy in the handwriting recognition task, and about 93% accuracy in the writer verification task based on four input images. We believe the proposed handwriting-based biometric technique has potential for use in a variety of online authentication services under the FIDO framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1435-1441
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• 2018

Online signature verification is one of the simple and efficient methods of identity verification and has less resistance than other biometric technologies. To handle signature verification as a classification problem, it is necessary to gather forgery signatures, which is not easy in most practical applications. It is not easy to obtain a large number of genuine signatures either. In this paper, one class SVM is used to tackle the forgery signature problem and someone else's signatures are used as general handwriting data to solve the genuine signature problem. Someone else's signature does not share shape-based features with the signature to be verified, but it contains the general characteristics of a signature and useful in verification. Verification rate can be improved by using the general handwriting data, which can be confirmed through the experimental results.

• ETRI Journal
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• v.37 no.1
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• pp.138-146
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• 2015

This paper presents a new non-constraining online optical handwritten signature reconstruction system that, in the main, makes use of a transparent glass pad placed in front of a color camera. The reconstruction approach allows efficient exploitation of hand activity during a signing process; thus, the system as a whole can be seen as a viable alternative to other similar acquisition tools. This proposed system allows people with physical or emotional problems to carry out their own signatures without having to use a pen or sophisticated acquisition system. Moreover, the developed reconstruction signature algorithms have low computational complexity and are therefore well suited for a hardware implementation on a dedicated smart system.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.316-321
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• 2017

We analyze the online Hangul handwriting recognition problem (HHR) and present solutions based on recurrent neural networks. The solutions are organized according to the three kinds of sequence labeling problem - sequence classifications, segment classification, and temporal classification, with additional consideration of the structural constitution of Hangul characters. We present a stacked gated recurrent unit (GRU) based model as the natural HHR solution in the sequence classification level. The proposed model shows 86.2% accuracy for recognizing 2350 Hangul characters and 98.2% accuracy for recognizing the six types of Hangul characters. We show that the type recognizing model successfully follows the type change as strokes are sequentially written. These results show the potential for RNN models to learn high-level structural information from sequential data.

• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.147-154
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• 2005

In conventional approaches such as a functinal approach or a parametric approach to online signature verification, which could not deal with the local shape of signature, much various important informations inherent in the local part of signature shape have been overlooked. In this paper, we try a structural approach in which a signature is represented as a structural form of handwriting primitives and the local parts along a signature handwriting can be selectively compared according to their discrimination power in the process of signature verification, As a result, the error rate is diminished in the case that the weights of subpattern units is applied into comparing process, which is the degree of discrimination power of local part. And also, the global variation and complexity of each signature extracted from the analysis of local shape is found useful in determining the decision threshold more precisely.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2298-2304
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• 2017

Online signature verification is one of the simple and efficient method of identity verification and has less resistance than other biometric technologies. In training to build a verification model, negative samples are required to build the model, but in most practical applications it is not easy to get negative samples - forgery signatures. In this paper, proposed is a method using someone else's signatures as negative samples. In verification, shape-based features extracted from the time-sequenced signature data are extracted and a support vector machine is used to verify. SVM tries to map a feature vector to a high dimensional space and to draw a linear boundary in the high dimensional space. SVM is one of the best classifiers and has been applied to various applications. Using general handwriting data, i.e., someone else's signatures which have little in common with positive samples improved the verification rate experimentally, which means that signature verification without negative samples is possible.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.325-341
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• 2015

This paper describes the Tezpur University dataset of online handwritten Assamese characters. The online data acquisition process involves the capturing of data as the text is written on a digitizer with an electronic pen. A sensor picks up the pen-tip movements, as well as pen-up/pen-down switching. The dataset contains 8,235 isolated online handwritten Assamese characters. Preliminary results on the classification of online handwritten Assamese characters using the above dataset are presented in this paper. The use of the support vector machine classifier and the classification accuracy for three different feature vectors are explored in our research.