Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

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Online and off-line handwriting recognition: a comprehensive survey

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

See, stats, and : https : / / www . researchgate . net / publication / 311886374 VUzzer : Application - aware Conference DOI : 10 . 14722 / ndss . 2017 . 23404 CITATIONS 0 READS 17 6 , including : Some : Systems Sanjay Vrije , Amsterdam , Netherlands 38 SEE Ashish International 1 SEE Cristiano VU 51 SEE Herbert VU 163 , 836 SEE All . The . All - text and , letting . Abstract—Fuzzing is an effective software testing technique to find bugs . Given the size and complexity of real - world applications , modern fuzzers tend to be either scalable , but not effective in exploring bugs that lie deeper in the execution , or capable of penetrating deeper in the application , but not scalable . In this paper , we present an application - aware evolutionary fuzzing strategy that does not require any prior knowledge of the application or input format . In order to maximize coverage and explore deeper paths , we leverage control - and data - flow features based on static and dynamic analysis to infer fundamental prop - erties of the application . This enables much faster generation of interesting inputs compared to an application - agnostic approach . We implement our fuzzing strategy in VUzzer and evaluate it on three different datasets : DARPA Grand Challenge binaries (CGC) , a set of real - world applications (binary input parsers) , and the recently released LAVA dataset . On all of these datasets , VUzzer yields significantly better results than state - of - the - art fuzzers , by quickly finding several existing and new bugs .

VUzzer: Application-aware Evolutionary Fuzzing.

Spectrum awareness is currently one of the most challenging problems in cognitive radio (CR) design. Detection and classification of very low SNR signals with relaxed information on the signal parameters being detected is critical for proper CR functionality as it enables the CR to react and adapt to the changes in its radio environment. In this work, the cycle frequency domain profile (CDP) is used for signal detection and preprocessing for signal classification. Signal features are extracted from CDP using a threshold-test method. For classification, a Hidden Markov Model (HMM) has been used to process extracted signal features due to its robust pattern-matching capability. We also investigate the effects of varied observation length on signal detection and classification. It is found that the CDP-based detector and the HMM-based classifier can detect and classify incoming signals at a range of low SNRs.

Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio

Online dynamic signature verification systems were designed and tested. A database of more than 10,000 signatures in (x(t), y(t))-form was acquired using a graphics tablet. We extracted a 42-parameter feature set at first, and advanced to a set of 49 normalized features that tolerate inconsistencies in genuine signatures while retaining the power to discriminate against forgeries. We studied algorithms for selecting and perhaps orthogonalizing features in accordance with the availability of training data and the level of system complexity. For decision making we studied several classifiers types. A modified version of our majority classifier yielded 2.5% equal error rate and, more importantly, an asymptotic performance of 7% false acceptance rate at zero false rejection rate, was robust to the speed of genuine signatures, and used only 15 parameter features.

/pdf/reliable-online-human-signature-verification-systems-10vei6hey5.pdf

Reliable online human signature verification systems

Together with the fundamentals of probability, random processes and statistical analysis, this insightful book also presents a broad range of advanced topics and applications. There is extensive coverage of Bayesian vs. frequentist statistics, time series and spectral representation, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) algorithm, geometric Brownian motion and Ito process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum–Welch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks are treated in detail. The book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, econometrics and mathematical finance. With a solutions manual, lecture slides, supplementary materials and MATLAB programs all available online, it is ideal for classroom teaching as well as a valuable reference for professionals.

Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance

Three methods for on-line signature verification are discussed in this paper. They are based on statistical models of features that summarize different aspects of signature shape and the dynamics o...

Statistical methods for on-line signature verification

Preface.- Notation.- Introduction.- Error Source Models.- Matrix Probabilities.- Model Parameter Estimation.- Performance of Forward Error-Correction Systems.- Performance Analysis of Communication Protocols.- Continuous Time HMM.- Continous State HMM.- Appendix 1.- Appendix 2.- Appendix 3.- Appendix 4.- Appendix 5.- Appendix 6.- Appendix 7.-Index

Performance Analysis and Modeling of Digital Transmission Systems

Probability, Random Processes, and Statistical Analysis: Random processes

A method for the automatic verification of handwritten signatures
is described. The method relies on global and local features that
summarize aspects of signature shape and dynamics of signature production.
We demonstrate that with the addition of our local feature based
on stroke direction coding, the performance of signature verification improves
significantly. The improvement is comparable with that of more
sophisticated algorithms, which require much more computer resources.
The current version of the program needs about 150 bytes to store a
signature model and has 3% equal error rate. At the 1% false rejection
point, the addition of the stroke direction information to the algorithm with
only global features reduces the false acceptance rate from 13 to 7.5%.
Prior to extraction of stroke direction information, signatures are normalized
for position, size, and orientation using their Fourier transform. Such
a scheme can also be useful in signature smoothing and compression.

William Turin

Papers

Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance

Statistical methods for on-line signature verification

Performance Analysis and Modeling of Digital Transmission Systems

Probability, Random Processes, and Statistical Analysis: Random processes

On‐line handwritten signature verification using stroke direction coding