Synthetic handwritten CAPTCHAs

Authors:
Achint Oommen Thomas;Amalia Rusu;Venu Govindaraju
Affiliations:
CUBS, CEDAR, State University of New York at Buffalo, 201 Bell Hall, Amherst, NY 14280, USA;CUBS, CEDAR, State University of New York at Buffalo, 201 Bell Hall, Amherst, NY 14280, USA;CUBS, CEDAR, State University of New York at Buffalo, 201 Bell Hall, Amherst, NY 14280, USA
Venue:
Pattern Recognition
Year:
2009

Citing 9
Cited 2

Piecewise Linear Skeletonization Using Principal Curves

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Stochastic Model Combining Discrete Symbols and Continuous Attributes and Its Application to Handwriting Recognition

DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Human Interactive Proofs and Document Image Analysis

DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Pessimal Print: A Reverse Turing Test

ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Generation of Synthetic Training Data for an HMM-based Handwriting Recognition System

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Combining shape and physical modelsfor online cursive handwriting synthesis

International Journal on Document Analysis and Recognition
A Human Interactive Proof Algorithm Using Handwriting Recognition

ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Building segmentation based human-friendly human interaction proofs (HIPs)

HIP'05 Proceedings of the Second international conference on Human Interactive Proofs
A highly legible CAPTCHA that resists segmentation attacks

HIP'05 Proceedings of the Second international conference on Human Interactive Proofs

Synthetic on-line signature generation. Part I: Methodology and algorithms

Pattern Recognition
A survey and analysis of current CAPTCHA approaches

Journal of Web Engineering

Quantified Score

Hi-index	0.01

Visualization

Abstract

CAPTCHAs (completely automated public Turing test to tell computers and humans apart) are in common use today as a method for performing automated human verification online. The most popular type of CAPTCHA is the text recognition variety. However, many of the existing printed text CAPTCHAs have been broken by web-bots and are hence vulnerable to attack. We present an approach to use human-like handwriting for designing CAPTCHAs. A synthetic handwriting generation method is presented, where the generated textlines need to be as close as possible to human handwriting without being writer-specific. Such handwritten CAPTCHAs exploit the differential in handwriting reading proficiency between humans and machines. Test results show that when the generated textlines are further obfuscated with a set of deformations, machine recognition rates decrease considerably, compared to prior work, while human recognition rates remain the same.