Cheung H. Leung

Off-line signature verification by the tracking of feature and stroke positions

There are inevitable variations in the signature patterns written by the same person. The variations can occur in the shape or in the relative positions of the characteristic features. In this paper, two methods are proposed to track the variations. Given the set of training signature samples, the first method measures the positional variations of the one-dimensional projection profiles of the signature patterns; and the second method determines the variations in relative stroke positions in the two-dimension signature patterns. The statistics on these variations are determined from the training set. Given a signature to be verified, the positional displacements are determined and the authenticity is decided based on the statistics of the training samples. For the purpose of comparison, two existing methods proposed by other researchers were implemented and tested on the same database. Furthermore, two volunteers were recruited to perform the same verification task. Results show that the proposed system compares favorably with other methods and outperforms the volunteers.

Analysis and recognition of Asian scripts-the state of the art

This paper summarizes the research activities of the pastdecade on the recognition of handwritten scripts used inChina, Japan, and Korea. It presents the recognitionmethodologies, features explored, databases used, andclassification schemes investigated. In addition, it includes adescription of the performance of numerous recognitionsystems found in both academic and industrial researchlaboratories. Recent achievements and applications are alsopresented. A list of relevant references is attached togetherwith our remarks on this subject.

Recognition of Handwritten Chinese Characters by Combining Regularization, Fisher's Discriminant and Distorted Sample Generation

The problem of offline handwritten Chinese character recognition has been extensively studied by many researchers and very high recognition rates have been reported. In this paper, we propose to further boost the recognition rate by incorporating a distortion model that artificially generates a huge number of virtual training samples from existing ones. We achieve a record high recognition rate of 99.46% on the ETL-9B database. Traditionally, when the dimension of the feature vector is high and the number of training samples is not sufficient, the remedies are to (i) regularize the class covariance matrices in the discriminant functions, (ii) employ Fishers dimension reduction technique to reduce the feature dimension, and (iii) generate a huge number of virtual training samples from existing ones. The second contribution of this paper is the investigation of the relative effectiveness of these three methods for boosting the recognition rate.

Improvement of Fingerprint Retrieval by a Statistical Classifier

The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous works resorted to simple k-nearest neighbor (k-NN) classification. However, the k-NN classifier has the drawbacks of being comparatively slow and less accurate. In this paper, we tackle this problem by first artificially expanding the set of training samples using our previously proposed spatial modeling technique. With the expanded training set, we are then able to employ a more sophisticated classifier such as the Bayes classifier for recognition. We apply the proposed method to the problem of one-to-N fingerprint identification and retrieval. The accuracy and speed are evaluated using the benchmarking FVC 2000, FVC 2002, and NIST-4 databases, and satisfactory retrieval performance is achieved.

A smoothness index based approach for off-line signature verification

Proposes a method to tackle the problem of detecting skilled forgeries in off-line signature verification. Inspired by the approach adopted by expert examiners, it is based on a smoothness criterion. From a collection of genuine and forged signatures, it is observed that, although skilled forgery signatures are very similar to genuine ones on a global scale, they are generally less smooth and natural on a detailed scale than the genuine ones, especially for those skilled forgery signatures which consist of cursive graphic patterns. A smoothness index is derived from such signatures. This is combined with other global shape features and used for verification. Satisfactory results are obtained.

Matching of complex patterns by energy minimization

Two patterns are matched by putting one on top of the other and iteratively moving their individual parts until most of their corresponding parts are aligned. An energy function and a neighborhood of influence are defined for each iteration. Initially, a large neighborhood is used such that the movements result in global features being coarsely aligned. The neighborhood size is gradually reduced in successive iterations so that finer and finer details are aligned. Encouraging results have been obtained when applied to match complex Chinese characters. It has been observed that computation increases with the square of the number of moving parts which is quite favorable compared with other algorithms. The method was applied to the recognition of handwritten Chinese characters. After performing the iterative matching, a set of similarity measures are used to measure the similarity in topological features between the input and template characters. An overall recognition rate of 96.1% is achieved.

Offline signature verification by the analysis of cursive strokes

In this paper, a method is proposed for offline signature verification. It is based on a smoothness criterion. It is observed that the cursive segments of forgery signatures are generally less smooth and less natural than the genuine ones, especially for those signatures that consist of cursive graphic patterns. Two approaches are proposed to extract a smoothness feature: a crossing method and a fractal dimension method. When the proposed smoothness feature is combined with other global shape features for signature verification, satisfactory results are obtained.

Segmentation and recognition of Chinese bank check amounts

Abstract. This paper describes a system for the recognition of legal amounts on bank checks written in the Chinese language. It consists of subsystems that perform preprocessing, segmentation, and recognition of the legal amount. In each step of the segmentation and recognition phases, a list of possible choices are obtained. An approach is adopted whereby a large number of choices can be processed effectively and efficiently in order to achieve the best recognition result. The contribution of this paper is the proposal of a grammar checker for Chinese bank check amounts. It is found to be very effective in reducing the substitution error rate. The recognition rate of the system is 74.0%, the error rate is 10.4%, and the reliability is 87.7%.

High-Performance Pentacene Thin-Film Transistor With High-

Pentacene organic thin-film transistor (OTFT) using high- k HfLaON gate dielectric is proposed, and the effects of varying its nitrogen content are studied. The HfLaON film is deposited using reactive sputtering of Hf-La target in Ar/O₂/N₂ ambience with different N₂ flow rates and then annealed in N₂. All the OTFTs can operate at low voltage with a threshold voltage as low as -0.53 V. The OTFT with an optimal nitrogen content can achieve a carrier mobility of 0.71 cm²/V·s, which is about twice that of its counterpart with HfLaO gate dielectric.

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The effects of La incorporation in three transition-metal (TM = Y, Zr, and Nb) oxides as gate dielectric of pentacene organic thin-film transistor (OTFT) have been investigated. La incorporated in Zr oxide and Nb oxide greatly decreases their trap density (as confirmed by low-frequency noise measurement) by passivating their oxygen vacancies, resulting in larger pentacene grains grown on them (as shown by atomic force microscopy) and thus higher carrier mobility for the OTFT due to less grain-boundary scattering. The carrier mobility of the ZrLaO- and NbLaO-OTFTs is about 70 times and 300 times higher than that of their counterparts based on ZrO2 and Nb2O5, respectively. However, La incorporated in Y2O3 increases its trap density by roughening its surface, causing smaller pentacene grains grown and thus lower carrier mobility. On the other hand, all the three TM elements incorporated in La2O3 can result in more moisture-resistant gate dielectric with smoother surface, resulting in larger pentacene grains grown and thus higher carrier mobility for the OTFT.