Abstract: 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.

Abstract: A hierarchical dictionary having methods of storing words based on frequency thereof in one or more documents that 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Storage volumes with historic values from document processing are used to create learn-sets for extraction engine training. Text and locations of the text in documents are obtained, such as with OCR routines or by retrieval from storage. The values of the storage volumes get matched to the text and the locations of the text are associated back to the values. Both the values and their locations are provided to extraction engine(s) for training. The form of the values and text may or may not match exactly. A degree of fuzziness matching occurs depending upon a type of value in storage. Types can be provided as user input, defined by entry in a database, or determined heuristically through characters found in the values and text. Merging of character fragments defines still other embodiments as does arranging executable code into modules for hardware, such as imaging devices.