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A probabilistic approach to printed document understanding


We propose an approach for information extraction for multi-page printed document understanding. The approach is designed for scenarios in which the set of possible document classes, i.e., documents sharing similar content and layout, is large and may evolve over time. Describing a new class is a very simple task: the operator merely provides a few samples and then, by means of a GUI, clicks on the OCR-generated blocks of a document containing the information to be extracted. Our approach is based on probability: we derived a general form for the probability that a sequence of blocks contains the searched information. We estimate the parameters for a new class by applying the maximum likelihood method to the samples of the class. All these parameters depend only on block properties that can be extracted automatically from the operator actions on the GUI. Processing a document of a given class consists in finding the sequence of blocks, which maximizes the corresponding probability for that class. We evaluated experimentally our proposal using 807 multi-page printed documents of different domains (invoices, patents, data-sheets), obtaining very good results—e.g., a success rate often greater than 90% even for classes with just two samples.

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Correspondence to Eric Medvet.

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Medvet, E., Bartoli, A. & Davanzo, G. A probabilistic approach to printed document understanding. IJDAR 14, 335–347 (2011).

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  • Document understanding
  • Automatic model upgrading
  • Invoice analysis
  • Maximum likelihood