Abstract: In some aspects, the disclosure is directed to methods and systems for automatic context-based annotation by leveraging a priori knowledge from annotations in template documents. A large library of template documents may be generated and pre-processed in many implementations to identify annotations or other inclusions commonly present on documents related to or conforming to the template. Newly scanned documents may be compared to these templates, and when a similar template is identified, annotation locations and types from the template may be applied to the newly scanned document to recognize and classify annotations and inclusions. To increase efficiency and provide scalability, comparisons of scanned documents and template documents may be distributed amongst a plurality of computing devices for processing in parallel, with similarity results aggregated.

Abstract: In some aspects, the disclosure is directed to methods and systems for automatic context-based annotation by leveraging a priori knowledge from annotations in template documents. A large library of template documents may be generated and pre-processed in many implementations to identify annotations or other inclusions commonly present on documents related to or conforming to the template. Newly scanned documents may be compared to these templates, and when a similar template is identified, annotation locations and types from the template may be applied to the newly scanned document to recognize and classify annotations and inclusions. To increase efficiency and provide scalability, comparisons of scanned documents and template documents may be distributed amongst a plurality of computing devices for processing in parallel, with similarity results aggregated.

Abstract: In some aspects, the disclosure is directed to methods and systems for machine learning-based document classification using multiple classifiers. Various classifiers may be employed during different iterations of the method to advance the classification of a document. The document may be classified and labeled in response to a predetermined number of classifiers agreeing upon a meaningful label. Further, the meaningful label may only be applied to the document in the event that the classifiers predicted the document label with a confidence score in excess of a threshold value.

Abstract: In some aspects, the disclosure is directed to methods and systems for automatic context-based annotation by leveraging a priori knowledge from annotations in template documents. A large library of template documents may be generated and pre-processed in many implementations to identify annotations or other inclusions commonly present on documents related to or conforming to the template. Newly scanned documents may be compared to these templates, and when a similar template is identified, annotation locations and types from the template may be applied to the newly scanned document to recognize and classify annotations and inclusions. To increase efficiency and provide scalability, comparisons of scanned documents and template documents may be distributed amongst a plurality of computing devices for processing in parallel, with similarity results aggregated.