In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

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Automatic Signature Verification: The State of the Art

Optical recognition of handwritten Chinese characters: Advances since 1980

A hand-based personal authentication using a coarse-to-fine strategy

In the information and communication personal verification is a crucial aspect. Among the different means for personal verification, handwritten signature plays a fundamental role since signature is the most diffuse means for personal verification in our daily life. In this paper a brief overview of the field of handwritten signature verification is presented and some of the most relevant perspectives are highlighted.

Verification of Handwritten Signatures: an Overview

In a process of signature verification, an off-line test signature is compared to one or more on-line reference signatures. Dynamic data obtained from the reference signature or signatures may be used to improve the thinning of the test signature. Such data may also be used for segmenting the test signature and sequencing the resulting strokes. In some embodiments, the invention includes evaluating a dynamic error, based at least in part on such sequencing of the test signature. In some further embodiments, the invention includes producing a rendering of at least one on-line reference signature as a two-dimensional image, and evaluating a static error based, at least in part, on shape-matching between the rendered image and the test signature.

Method and apparatus for verifying static signatures using dynamic information

We propose a new segmenting method for handwritten Chi- nese signatures based on the wavelet transform for signature verifica- tion. There are some differences in identifying a handwritten signature and in recognizing a handwritten character because there are meaning- ful features hidden in writing habits when an individual is signing his or her signature. These features exhibit themselves in the pen-down, pen- up, and in the corner of a stroke. Therefore the segmentation for identi- fying a signature and for recognizing a character should be different even though the same characters are involved. We propose to segment an input signature curve at the inflection points, and we locate the inflec- tion points by detecting the zero-crossing points of the wavelet trans- forms of the input signature. Experimental results show that this new segmenting method has better segmentation capability than other meth- ods that are usually used. © 1996 Society of Photo-Optical Instrumentation Engi- neers.

Optimal segmentation of handwritten Chinese signatures using wavelet transforms

An intelligent optical Chinese character recognition system using accumulated stroke features has been developed to solve the input problem of Chinese characters. The hardware architecture of the system is built on an IBM PC-AT with three extension boards: the preprocessor board, the feature extraction board, and the matching recognition board. The system can recognize, at the same time in the same program, either printed or handwritten Chinese characters of different styles and sizes. At present, a total of 5401 commonly used Chinese characters can be recognized. Results show that 99% of printed characters and 90% of constrained handwritten characters can be correctly recognized, at a speed of about 300 characters per minute.

Optical Chinese Character Recognition Using Accumulated Stroke Features

We propose an on-line Chinese signature verification system based on mixture of experts to further improve the reliability of a signature verification system. As we know, the signature of a certain signer has some personal particular characteristics. We collect these characteristics, and classify them into several feature vectors. In our system, an individual feature vector corresponds to an expert which performs the function of signature verification. According to the particular habit of an individual signer, an individual expert with a particular feature vector plays a different role with different power to a different signer. These verification results should be weighted by a weighted controller. The function of the weighted controller works as the personal habit function. In our proposed system, there are seven verification experts with different feature vectors to make their verification decision. Based on these results, the combination unit with a weighted controller makes the final decision. The weighted controller controls the weight of the decision between the experts and the individual signer. Then, the combination of mixture results of the seven experts is attained, and a signature verification system with a personal-oriented feature decision is approached. Finally, the performance of our system is demonstrated in several experiments which show a high success rate of verification.

https://ieeexplore.ieee.org/iel4/5863/15619/00723799.pdf

On-line Chinese signature verification with mixture of experts

A tremor detection scheme for handwritten signature verification is proposed. Based on the multi-resolution property of a wavelet transform, it can extract the trembling features of handwritten signatures. The scheme resolves the problem of distinguishing skilled forgery signatures from genuine signatures.

Tremor detection of handwritten Chinese signatures based on multiresolution decomposition using wavelet transforms

We propose an attributed string matching approach based on the writing sequences of an input signature for Chinese signature verification. It is impossible to find features of a signature that are invariant with respect to individual writing style. The aim of our research is to find a particular feature set that will exhibit small intraclass variance. A signer tends to connect consecutive strokes in a constant sequences when signing a signature, especially for Chinese signatures. Therefore, the writing sequences (stroke order) of a signature can be regarded as the personal signature feature. In order to obtain an attributed string that will be used in the string matching similarity calculation, we must split an input signature into several segments from the corners of strokes at first. Since the wavelet transform has been used in the field of edge detection and corner detection for a long time, we can use it as a tool for corner detection to find an optimal segmentation set. Therefore, an attributed string for consecutive segments can be calculated after the signature has been split into several segments suitably by means of a wavelet transform. The elements in the attributed vector include relative angle of adjoining segments, the direction code of the segment, writing duration time of the segment, and the length of the segment. The total relative angle can also be summarized over all of the relative angles of segments to result in another individual feature. This total relative angle can be used to filter the rougher forgery signatures. Our attributed string matching method is also based on the extraction of irreducible characteristic points. The experimental results show a very excellent discrimination capability.

http://ieeexplore.ieee.org/iel4/5863/15619/00723798.pdf

Bor-Shenn Jeng

Papers

Optimal segmentation of handwritten Chinese signatures using wavelet transforms

Optical Chinese Character Recognition Using Accumulated Stroke Features

On-line Chinese signature verification with mixture of experts

Tremor detection of handwritten Chinese signatures based on multiresolution decomposition using wavelet transforms

Handwritten Chinese signature verification based on attributed string matching of stroke linkage order