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Human vision is able to compensate imperfections in sensory inputs from thereal world by reasoning based on prior knowledge about the world. Machinelearning has had a significant impact on computer vision due to its inherentability in handling imprecision, but the absence of a reasoning framework basedon domain knowledge limits its ability to interpret complex scenarios. Wepropose semi-lexical languages as a formal basis for dealing with imperfecttokens provided by the real world. The power of machine learning is used to mapthe imperfect tokens into the alphabet of the language and symbolic reasoningis used to determine the membership of input in the language. Semi-lexicallanguages also have bindings that prevent the variations in which asemi-lexical token is interpreted in different parts of the input, therebyleaning on deduction to enhance the quality of recognition of individualtokens. We present case studies that demonstrate the advantage of using such aframework over pure machine learning and pure symbolic methods.

Semi-Lexical Languages -- A Formal Basis for Unifying Machine Learning and Symbolic Reasoning in Computer Vision