Machine learning of document templates for data extraction

A method for generalizing a document template for data extraction, comprising:

• receiving descriptions of one or more data elements;
• receiving a document template providing a generalized description of the one or more data elements,
• receiving a training document for improving the extraction capabilities of the document template;
• and
• for each data element,
• determining whether the document template correctly predicts the data element, and
• if the data element is not correctly predicted, generalizing the data element description in the document template.

A system for training a computer system to extract data from an image, comprising:

• a stream of training examples;
• a prediction module that receives the stream of training examples and predicts the location of data elements within a training example;
• input data indicating the location of data elements;
• and
• a generalization module using the input data to create a template used to extract data from an image, wherein the generalization module generalizes the document template only if the prediction module does not predict the proper location of each of the data elements.

A computer-readable medium comprising a template for extracting data elements from an image, the template comprising a plurality of data element definitions, each data element definition including one or more data element generalizations, the one or more data element generalizations being based upon positional data and character data, wherein the positional data includes one or more from the group consisting of:

• physical attributes;
• lexical attributes;
• contextual attributes;
• and control attributes, wherein the contextual attributes include a total number of words in a context, and one or more context words, each context word having one or more associated measurements.