Abstract: A volume of an object is extracted from a three-dimensional image to generate a three-dimensional object image, where the three-dimensional object image represents the object but little to no other aspects of the three-dimensional image. The three-dimensional image is yielded from an examination in which the object, such as a suitcase, is situated within a volume, such as a luggage bin, that may contain other aspects or objects that are not of interest, such as sidewalls of the luggage bin. The three-dimensional image is projected to generate a two-dimensional image, and a two-dimensional boundary of the object is defined, where the two-dimensional boundary excludes or cuts off at least some of the uninteresting aspects. In some embodiments, the two-dimensional boundary is reprojected over the three-dimensional image to generate a three-dimensional boundary, and voxels comprised within the three-dimensional boundary are extracted to generate the three-dimensional object image.

Abstract: Methods and apparatuses are described for automatic classification of error conditions in automated user interface testing. A server receives an image file corresponding to a current user interface (UI) view of a software application under test. The server analyzes the image file to identify error conditions that exist in the current UI view. The server assigns a classification to the image file according to one or more error types based upon the error conditions identified in the current UI view. The server transmits a notification message to one or more remote computing devices, the notification message comprising the image file and the classification assigned to the image file.

Abstract: A volume of an object is extracted from a three-dimensional image to generate a three-dimensional object image, where the three-dimensional object image represents the object but little to no other aspects of the three-dimensional image. The three-dimensional image is yielded from an examination in which the object, such as a suitcase, is situated within a volume, such as a luggage bin, that may contain other aspects or objects that are not of interest, such as sidewalls of the luggage bin. The three-dimensional image is projected to generate a two-dimensional image, and a two-dimensional boundary of the object is defined, where the two-dimensional boundary excludes or cuts off at least some of the uninteresting aspects. In some embodiments, the two-dimensional boundary is reprojected over the three-dimensional image to generate a three-dimensional boundary, and voxels comprised within the three-dimensional boundary are extracted to generate the three-dimensional object image.

Abstract: In a document analysis system that receives and processes jobs from a plurality of users, in which each job may contain multiple electronic documents, to extract data from the electronic documents, a method of automatically pre-processing each received electronic document using a plurality of image transformation algorithms to improve subsequent data extraction from said document is provided. The method includes: electronically partitioning each received electronic document page into pieces; automatically processing each piece of the received electronic document page using each of a plurality of image pre-processing algorithms to produce a plurality of image variations of each piece; and analyzing the outputs of subsequent processing and data extraction, on each of the image variations of the pieces to determine which output is best, from the plurality of outputs for each piece.

Abstract: In a document analysis system that receives and processes jobs from a plurality of users, in which each job may contain multiple electronic documents, to extract data from the electronic documents, a method of automatically pre-processing each received electronic document using a plurality of image transformation algorithms to improve subsequent data extraction from said document is provided. The method includes: electronically partitioning each received electronic document page into pieces; automatically processing each piece of the received electronic document page using each of a plurality of image pre-processing algorithms to produce a plurality of image variations of each piece; and analyzing the outputs of subsequent processing and data extraction, on each of the image variations of the pieces to determine which output is best, from the plurality of outputs for each piece.

Abstract: In a document analysis system that receives and processes jobs from a plurality of users, in which each job may contain multiple electronic documents, to extract data from the electronic documents, a method of automatically extracting data from each received electronic document using a plurality of character recognition engines is provided. The method includes: automatically processing each received electronic document page using each of a plurality of recognition engines to extract data; comparing quality of data extracted from each of the recognition engines to assign a confidence score to the extracted data; and selecting extracted data having highest confidence score as the correct extracted data.

Abstract: In a document analysis system that receives and processes jobs from a plurality of users, in which each job may contain multiple electronic documents, to extract data from the electronic documents, a method of automatically pre-processing each received electronic document using a plurality of image transformation algorithms to improve subsequent data extraction from said document is provided. The method includes: electronically partitioning each received electronic document page into pieces; automatically processing each piece of the received electronic document page using each of a plurality of image pre-processing algorithms to produce a plurality of image variations of each piece; and analyzing the outputs of subsequent processing and data extraction, on each of the image variations of the pieces to determine which output is best, from the plurality of outputs for each piece.

Abstract: A method of automatically extracting data from an electronic document containing a plurality of layout features through progressive refinement is provided. The method includes: analyzing each document to automatically extract images and text features wherein each document includes at least two features that are related to each other, and wherein said analyzing compares extracted features with a first search space of candidate features to try and recognize the extracted features; if one of the at least two related features is not recognized and at least one feature is recognized, selecting a second search space of candidate features in response thereto and in response to predefined rules about the relationship between the two features; and comparing the unrecognized feature with said selected second search space.

Abstract: A method of training a document analysis system to extract data from documents is provided. The method includes: automatically analyzing images and text features extracted from a document to associate the document with a corresponding document category; comparing the extracted text features with a set of text features associated with corresponding category of the document, in which the set of text features includes a set of characters, words, and phrases; if the extracted features are found to consist of the characters, words, and phrases belonging to the set of text features associated with the corresponding document category, storing the extracted text features as the data contained in the corresponding document; and, if the extracted text features are found to include at least one text feature that does not belong to the set of text features associated with the corresponding document category, submitting the unrecognized text features to a training phase.