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PessimalPrint: a reverse Turing test

Abstract. We exploit the gap in ability between human and machine vision systems to craft a family of automatic challenges that tell human and machine users apart via graphical interfaces including Internet browsers. Turing proposed [Tur50] a method whereby human judges might validate “artificial intelligence” by failing to distinguish between human and machine interlocutors. Stimulated by the “chat room problem” posed by Udi Manber of Yahoo!, and influenced by the CAPTCHA project [BAL00] of Manuel Blum et al. of Carnegie-Mellon Univ., we propose a variant of the Turing test using pessimal print: that is, low-quality images of machine-printed text synthesized pseudo-randomly over certain ranges of words, typefaces, and image degradations. We show experimentally that judicious choice of these ranges can ensure that the images are legible to human readers but illegible to several of the best present-day optical character recognition (OCR) machines. Our approach is motivated by a decade of research on performance evaluation of OCR machines [RJN96,RNN99] and on quantitative stochastic models of document image quality [Bai92,Kan96]. The slow pace of evolution of OCR and other species of machine vision over many decades [NS96,Pav00] suggests that pessimal print will defy automated attack for many years. Applications include `bot' barriers and database rationing.

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Received: February 14, 2002 / Accepted: March 28, 2002

An expanded version of: A.L. Coates, H.S. Baird, R.J. Fateman (2001) Pessimal Print: a reverse Turing Test. In: {\it Proc. 6th Int. Conf. on Document Analysis and Recognition}, Seattle, Wash., USA, September 10–13, pp. 1154–1158

Correspondence to: H. S. Baird

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Baird, H., Coates, A. & Fateman, R. PessimalPrint: a reverse Turing test. IJDAR 5, 158–163 (2003).

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