Abstract: In some embodiments, a method includes identifying a set of word bounding boxes in a first electronic document, and identifying locations of horizontal white space between two adjacent rows from a set of rows in a table. The method includes determining, using a Natural Language Processing algorithm, an entity name from a set of entity names for each table cell from a set of table cells in the table. The method includes determining, using a machine learning algorithm a class from a set of classes for each row from the set of rows. The method includes extracting a set of table cell values associated with the set of table cells, and generating a second electronic document including the set of table cell values arranged in the set of rows and the set of columns such that the set of words in the table are computer-readable in the second electronic document.

Abstract: In some embodiments, a method includes identifying a set of word bounding boxes in a first electronic document, and identifying locations of horizontal white space between two adjacent rows from a set of rows in a table. The method includes determining, using a Natural Language Processing algorithm, an entity name from a set of entity names for each table cell from a set of table cells in the table. The method includes determining, using a machine learning algorithm a class from a set of classes for each row from the set of rows. The method includes extracting a set of table cell values associated with the set of table cells, and generating a second electronic document including the set of table cell values arranged in the set of rows and the set of columns such that the set of words in the table are computer-readable in the second electronic document.

Abstract: In some embodiments, a method includes identifying a set of word bounding boxes in a first electronic document, and identifying locations of horizontal white space between two adjacent rows from a set of rows in a table. The method includes determining, using a Natural Language Processing algorithm, an entity name from a set of entity names for each table cell from a set of table cells in the table. The method includes determining, using a machine learning algorithm a class from a set of classes for each row from the set of rows. The method includes extracting a set of table cell values associated with the set of table cells, and generating a second electronic document including the set of table cell values arranged in the set of rows and the set of columns such that the set of words in the table are computer-readable in the second electronic document.

Abstract: In some embodiments, a method includes identifying a set of word bounding boxes in a first electronic document, and identifying locations of horizontal white space between two adjacent rows from a set of rows in a table. The method includes determining, using a Natural Language Processing algorithm, an entity name from a set of entity names for each table cell from a set of table cells in the table. The method includes determining, using a machine learning algorithm a class from a set of classes for each row from the set of rows. The method includes extracting a set of table cell values associated with the set of table cells, and generating a second electronic document including the set of table cell values arranged in the set of rows and the set of columns such that the set of words in the table are computer-readable in the second electronic document.

Abstract: In some embodiments, an apparatus includes a memory and a processor operatively coupled to the memory. The processor is configured to receive an electronic document having a set of pages, and partition a page from the set of pages of the electronic document into a set of portions. The processor is configured to convert each portion of the set of portions into a negative image of a set of negative images. The processor is configured to produce, based on an artificial intelligence algorithm, a de-noised negative image of each negative image and convert each de-noised negative image of a set of de-noised negative images into a positive image of a set of positive images, and combine each positive image of the set of positive images to produce a de-noised page. The de-noised page has artifacts less than artifacts of the page of the electronic document.

Abstract: Systems and methods provide acceleration of automatic test pattern generation in a multi-core computing environment via multi-level parameter value optimization for a parameter set with speculative scheduling. The methods described herein use multi-core based parallel runs to parallelize sequential execution, speculative software execution to explore possible parameter sets, and terminate/prune runs when the optimum parameter value is found at a previous level. The present invention evaluates the design prior to the implementation of the compression IP so that it can define the configuration of DFT and ATPG to maximize the results of compression as measured by test data volume and test application time.

Abstract: Systems and methods provide acceleration of automatic test pattern generation in a multi-core computing environment via multi-level parameter value optimization for a parameter set with speculative scheduling. The methods described herein use multi-core based parallel runs to parallelize sequential execution, speculative software execution to explore possible parameter sets, and terminate/prune runs when the optimum parameter value is found at a previous level. The present invention evaluates the design prior to the implementation of the compression IP so that it can define the configuration of DFT and ATPG to maximize the results of compression as measured by test data volume and test application time.