Google Inc.

Igor Krivokon

Dar-Shyang Lee

Luc Vincent

Vladimir Ofitserov

Oleg Kislyuk

Methods, systems, and apparatus for computing quality scores based on neighbor features. In one aspect, a method includes obtaining a quality model that was trained using a set of training entities; identifying a set of candidate entities that are different from each of the training entities; for each candidate entity: obtaining a first quality score for the candidate entity; obtaining one or more neighbor features for neighbor entities of the candidate entity, where each neighbor entity of the candidate entity is linked to the candidate entity; obtaining one or more entity specific feature values for the candidate entity, where each entity specific feature value is determined independent of the neighbor entities of the candidate entity; and determining a second quality score for the candidate entity using the quality model, the second quality score being computed based on the first quality score, the neighbor features, and the entity specific feature values.

Evaluating quality based on neighbor features

Various aspects can be implemented for determining optical character recognition (OCR) parameters using an OCR engine. In general, one aspect can be a method that includes using an optical character recognition (OCR) engine in a base configuration to generate one or more OCR responses corresponding to one or more sample pages of a document. The method also includes identifying a dominant OCR parameter for the document based on the one or more generated OCR responses. Other implementations of this aspect include corresponding systems, apparatus, and computer program products.

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Belmont, CA, United States of America

Igor Krivokon