A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
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We propose to adapt Turing's seminal 1950 test for machine intelligence to evaluating progress in document analysis systems. Our premise is that a problem can be considered solved if automated and human solutions to the underlying task are indistinguishable to a skeptical human judge. For the domain-specific problems of concern here, we reformulate the test to keep the interaction between judges and human/machine participants to graphical user interfaces that do not require natural language processing, a notable difference from Turing's original formulation. Examples of tasks that may lend themselves to such tests include detecting or identifying specific document components such as logos, photographs, tables, as well as writer and language identification. The administration of the test would be facilitated by commercial crowd-sourcing systems such as Amazon Mechanical Turk, as well as research platforms such as the Lehigh Document Analysis Engine (DAE) that accept arbitrary documents for input, record test results, and provide for trusted execution of submitted programs.