Palo Alto Research Center Inc.

Yizhou Wang

Minh Binh Do

Haitham Hindi

Dashan Gao

Jindong Chen

A computer-implemented system and method for retrieving a digital image through document image decomposition is provided. A stored digital image is retrieved. Generic visual features are extracted. The features are grouped into a primitive layer including word-graphs that each include words and features. The words are grouped into a layout layer including zone hypotheses that each include one or more of the words. Causal dependencies between the word-graphs and the zone hypotheses are expressed through zone models that include a joint probability defining a pair of probabilistic models generated through a learned binary edge classifier. Each pair of probabilistic models is expressed as an optimal set selection problem including a set of cost functions and constraints. The optimal set selection problem is evaluated through a heuristic search of the cost functions and constraints and a non-overlapping optimal set of the zone hypotheses is provided that characterize the stored digital image.

Computer-implemented system and method for recognizing patterns in a digital image through document image decomposition

A method employing a hybrid classification model is used to perform optical character recognition operations for an image. Image data from the image is provided to a generative classification model of the hybrid model, and generative image classifications operations are performed, generating a feature data set which is outputted from the generative classification model. This feature data set is then provided to the discriminative classification model, and discriminative classification operations are performed to generate a classification of the image.

Innovative OCR systems and methods that combine a template based generative model with a discriminative model

A system and method for decomposing a digital image is provided. A digital image is represented as a word-graph, which includes words and visualized features, and zone hypotheses that group one or more of the words. Causal dependencies of the zone hypotheses are expressed through a learned generative zone model to which costs and constraints are assigned. An optimal set of the zone hypotheses are inferred, which are non-overlapping, through a heuristic search of the costs and constraints.

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Sunnyvale, CA, United States of America

Yizhou Wang