Other

Hyland Software Inc.

Hyland Switzerland Sarl

Ser Solutions, Inc.

Kofax International Switzerland Sarl

Bdgb Enterprise Software S.a.r.l.

Lexmark International Technology, Sa

Harry Urbschat

Ralph Meier

Thorsten Wanschura

Johannes Hausmann

Gannady Lapir

Pal Ruján

Gennady Lapir

Pal Rujan

Pál Ruján

Described herein are various technologies pertaining to text extraction from a document. A computing device receives the document. The document comprises computer-readable text and a layout, wherein the layout defines positions of the computer-readable text. Responsive to receiving the document, the computing device identifies at least one textual element in the computer-readable text based upon spatial factors between portions of the computer-readable text and contextual relationships between the portions of the computer-readable text. The computing device then outputs the at least one textual element.

Computing system for extraction of textual elements from a document

A hierarchical dictionary having methods of storing words based on frequency thereof in one or more documents which includes the steps of identifying a hash value corresponding to an inputted word; storing the word in a first hash map and in a second hash map having a substantially larger word storage capacity than the first hash map based on the identified hash value; clearing the first hash map at every predetermined period or triggering event; determining whether a frequency of the word as stored in the second hash map exceeds a predetermined value; and if so, promoting the word from the second hash map to a third hash map having a substantially larger word storage capacity than the second hash map for long-term storage and later retrieval.

Hierarchical dictionary with statistical filtering based on word frequency

Described herein are various technologies pertaining to text extraction from a document. A computing device receives the document. The document comprises computer-readable text and a layout, wherein the layout defines positions of the computer-readable text within a two-dimensional area represented by the document. Responsive to receiving the document, the computing device identifies at least one textual element in the computer-readable text based upon spatial factors between portions of the computer-readable text and contextual relationships between the portions of the computer-readable text. The computing device then outputs the at least one textual element.

Methods and apparatus delineate grouped together content in documents. Void and unvoid pixels in document images get clustered together. Execution of a histogram and autocorrelation function, including peak detection, against the unvoid clusters reveals the content. Techniques for clustering include iteratively transforming an original image into secondary images with a Haar wavelet transformation, for example. Clustering begins on a lowest image plane and advances to a next highest plane until all void and unvoid pixels in the images are grouped. Void clusters at lower levels remain void clusters at higher levels, thus only unvoid clusters of pixels require processing at higher levels thereby optimizing processing. Imaging devices with scanners define suitable hardware for transformation of the document into images and processors with executable code cluster together pixels to delineate content. Further processing includes executing OCR or other routines post void/unvoid analysis.

Content delineation in document images

Methods to select and extract tabular data among the optical character recognition returned strings to automatically process documents, including documents containing academic transcripts.

Methods for automatic structured extraction of data in OCR documents having tabular data

Systems and methods for determining a location of a target in a document. Information compiled from a training document is created, the information comprising a reference and a reference vector tying each reference to the target. The reference is compared to a new reference in a new document to determine if there are any similar references that are the target, wherein similar references are: position similar, or type similar, or both. When the new reference comprises a typo, an optical character recognition (OCR) mistake, or both, the new reference is still determined to be the target because of the new reference's location.

System and method of using dynamic variance networks

A method and system for determining at least one target value of at least one target in at least one document, comprising: determining, utilizing at least one scoring application; at least one possible target value, wherein the at least one scoring application utilizes information from at least one training document, and applying the information, utilizing the at least one scoring application, on the at least one new document to determine at least one value of the at least one target on the at least one new document.

System and method for obtaining document information

A system and/or method for increasing the accuracy of optical character recognition (OCR) for at least one item, comprising: obtaining OCR results of OCR scanning from at least one OCR module; creating at least one OCR seed using at least a portion of the OCR results; creating at least one OCR learn set using at least a portion of the OCR seed; and applying the OCR learn set to the at least one item to obtain additional optical character recognition (OCR) results.

System and method for increasing the accuracy of optical character recognition (OCR)

A system and method for extracting information from at least one document in at least one set of documents, the method comprising: generating, using at least one ranking and/or matching processor, at least one ranked possible match list comprising at least one possible match for at least one target entry on the at least one document, the at least one ranked possible match list based on at least one attribute score and at least one localization score.

Method and system for extraction

A method for building a classification model for classifying unclassified documents based on the classification of a plurality of documents which respectively have been classified as belonging to one of a plurality of classes, said documents being digitally represented in a computer, said documents respectively comprising a plurality of terms which respectively comprise one or more symbols of a finite set of symbols, and said method comprising the following steps: representing each of said plurality of documents by a vector of n dimensions, said n dimensions forming a vector space, whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document corresponding to said vector, so that said n dimensions span up a vector space; representing the classification of said already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes, such that each subspace comprises one or more documents as represented by their corresponding vectors in said vector space, so that said each subspace corresponds to a class.

Growing community of inventors

Oldenburg, Germany

Harry Urbschat