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公开(公告)号：US10713519B2
公开(公告)日：2020-07-14
申请号：US15630779
申请日：2017-06-22
Applicant: ADOBE INC.
Inventor： Trung Huu Bui , Hung Hai Bui , Shawn Alan Gaither , Walter Wei-Tuh Chang , Michael Frank Kraley , Pranjal Daga
Abstract: The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.
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公开(公告)号：US11769111B2
公开(公告)日：2023-09-26
申请号：US16904881
申请日：2020-06-18
Applicant: ADOBE INC.
Inventor： Trung Huu Bui , Hung Hai Bui , Shawn Alan Gaither , Walter Wei-Tuh Chang , Michael Frank Kraley , Pranjal Daga
IPC: G06F17/00 , G06Q10/10 , G06Q10/06 , G06F40/10 , G06V30/148 , G06V30/413 , G06F40/103
CPC classification number: G06Q10/10 , G06F40/10 , G06F40/103 , G06Q10/06 , G06V30/153 , G06V30/413
Abstract: The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.
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公开(公告)号：US10372821B2
公开(公告)日：2019-08-06
申请号：US15462684
申请日：2017-03-17
Applicant: Adobe Inc.
Inventor： Walter Chang , Trung Bui , Pranjal Daga , Michael Kraley , Hung Bui
Abstract: Certain embodiments identify a correct structured reading-order sequence of text segments extracted from a file. A probabilistic language model is generated from a large text corpus to comprise observed word sequence patterns for a given language. The language model measures whether splicing together a first text segment with another continuation text segment results in a phrase that is more likely than a phrase resulting from splicing together the first text segment with other continuation text segments. Sets of text segments, which include a first set with a first text segment and a first continuation text segment as well as a second set with the first text segment and a second continuation text segment, are provided to the probabilistic model. A score indicative of a likelihood of the set providing a correct structured reading-order sequence is obtained for each set of text segments.
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4.发明申请公开(公告)号：US20200320329A1
公开(公告)日：2020-10-08
申请号：US16904881
申请日：2020-06-18
Applicant: ADOBE INC.
Inventor： Trung Huu Bui , Hung Hai Bui , Shawn Alan Gaither , Walter Wei-Tuh Chang , Michael Frank Kraley , Pranjal Daga
Abstract: The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.
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