Abstract: A method comprises determining instance bounds associated with each of one or more structural elements in a document using a machine learning model. The method further comprises determining an error in the instance bounds associated with a particular one of the one or more structural elements. The method further comprises correcting the error in the instance bounds associated with the particular structural element using document content associated with the particular structural element, thereby generating corrected instance bounds associated with the particular structural element. The method further comprises generating a structural map of the document based on the corrected instance bounds.

Abstract: A method comprises determining instance bounds associated with each of one or more structural elements in a document using a machine learning model. The method further comprises determining an error in the instance bounds associated with a particular one of the one or more structural elements. The method further comprises correcting the error in the instance bounds associated with the particular structural element using document content associated with the particular structural element, thereby generating corrected instance bounds associated with the particular structural element. The method further comprises generating a structural map of the document based on the corrected instance bounds.

Abstract: Techniques are disclosed for identifying document structural elements and correcting errors in the classification and/or location of the identified structural elements. An example method includes determining location and classification for a structural element on a page of the document using a machine learning (ML) model; determining one or more errors in the location and/or classification for the structural element; and correcting each instance of the one or more errors using other content in the document (e.g., content spatially adjacent to the corresponding structural element on the page of the document). The method may further include storing the document and the location and classification (as corrected), and/or generating a structural map of the page of the document based on the location and classification (as corrected). The use of the document content to correct errors greatly enhances the agreement between the identified structural elements and the original document.

Abstract: Techniques are disclosed for identifying document structural elements and correcting errors in the classification and/or location of the identified structural elements. An example method includes determining location and classification for a structural element on a page of the document using a machine learning (ML) model; determining one or more errors in the location and/or classification for the structural element; and correcting each instance of the one or more errors using other content in the document (e.g., content spatially adjacent to the corresponding structural element on the page of the document). The method may further include storing the document and the location and classification (as corrected), and/or generating a structural map of the page of the document based on the location and classification (as corrected). The use of the document content to correct errors greatly enhances the agreement between the identified structural elements and the original document.

Abstract: Techniques and systems for auditing and augmenting user-generated tags for digital content are described. A corpus is generated for each tag associated with digital content to represent different aspects of the tag's definition. A hierarchy of semantic relationships between the tags is created based a corpus co-occurrence between the tags to relate at least two tags having different levels of specificity. Some of the tags are verified with the digital content based on feature detection, and a correspondence to the digital content is determined for each verified tag. The correspondence of the verified tags is then propagated up the hierarchy to others of the tags that are semantically related to the verified tags but which were not verified by the feature detection. The verified tags and the verified other tags are then assigned to the digital content to control how the digital content is retrieved when subject to a search.

Abstract: Techniques and systems for auditing and augmenting user-generated tags for digital content are described. A corpus is generated for each tag associated with digital content to represent different aspects of the tag's definition. A hierarchy of semantic relationships between the tags is created based a corpus co-occurrence between the tags to relate at least two tags having different levels of specificity. Some of the tags are verified with the digital content based on feature detection, and a correspondence to the digital content is determined for each verified tag. The correspondence of the verified tags is then propagated up the hierarchy to others of the tags that are semantically related to the verified tags but which were not verified by the feature detection. The verified tags and the verified other tags are then assigned to the digital content to control how the digital content is retrieved when subject to a search.