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optical character recognition (ocr) features  (ABBYY USA Software House Inc)

 
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    Structured Review

    ABBYY USA Software House Inc optical character recognition (ocr) features

    Optical Character Recognition (Ocr) Features, supplied by ABBYY USA Software House Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/optical+character+recognition+function/optical+character+recognition+software/pmc07859404-247-6-13
    Average 90 stars, based on 1 article reviews
    optical character recognition (ocr) features - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "Building schematic of Vienna in the late 1920s"

    Article Title: Building schematic of Vienna in the late 1920s

    Journal: Scientific Data

    doi: 10.1038/s41597-021-00822-0


    Figure Legend Snippet:

    Techniques Used: Expressing

    Related Articles

    other:

    Article Title: Annual and state-level data on U.S. total fertility rates, 1931–present
    Article Snippet: Fig 1 Data transcription was performed using Optical Character Recognition (OCR) analysis through ABBYY software, transcribing the number of births for each state-year, categorized by maternal age and stratified by race.

    Article Title: Methods and systems for artificial intelligence- assisted document annotation
    Article Snippet: As illustrated in FIG. 24, the enterprise services platform may comprise a plurality of components or layers, e.g., (i) an AI services platform, (ii) an AI-driven annotation (AIDA) application running on the AI services platform, (iii) additional software modules (e.g., ABBYY optical character recognition, flexible document layout extraction tools, template-based document extraction tools, etc.), (iv) an AI computing platform that supports the AI services, (v) a data platform, and (vi) an identity and access management layer.

    Article Title: Building schematic of Vienna in the late 1920s
    Article Snippet: Online-only Table ) and applied the optical character recognition (OCR) features of the ABBYY FineReader software ( https://www.abbyy.com/en-eu/finereader/ ).

    Article Title: Methods and systems for artificial intelligence-assisted document annotation
    Article Snippet: The Schedule K-1 prediction process comprises four main steps: (i) the electronic Schedule K-1 documents are processed using the FlexiCapture Engine (FCEng), which combines ABBYY FlexiCapture, ABBYY FineReader, and ABBYY Optical Character Recognition (OCR) capabilities, to produce XML or JTD files; (ii) a trained Statement/Footnote Classifier model segments the text within the document into statement or footnote categories; (iii) a trained Statement Extractor model predicts which text corresponds to the statement labels, and a trained Footnote Extractor model predicts which text corresponds to the footnote labels.

    Software:

    Article Title: Patient perspectives on same-day discharge following catheter ablation for atrial fibrillation: results from a patient survey as part of the monocentric FAST AFA trial
    Article Snippet: .. Handouts were collected from patients before regular hospital discharge and processed via an optical character recognition software (ABBYY, ic solutions GmbH, Leipzig, Germany). ..

    Article Title: Patient perspectives on same-day discharge following catheter ablation for atrial fibrillation: results from a patient survey as part of the monocentric FAST AFA trial.
    Article Snippet: Patient-reported outcome measures included the patientreported outcomes measurement information system (PROMIS) global health,15 patient health questionnaire-2 (PHQ-2),16 and the atrial fibrillation effect on quality-of-life questionnaire (AFEQT).17 All patients were informed in detail about the medical background, the practical implementation of SDD concepts (including selection criteria for SDD, see Supplementary material online, Supplementary Material), and the existing scientific data in this context. .. Handouts were collected from patients before regular hospital discharge and processed via an optical character recognition software (ABBYY, ic solutions GmbH, Leipzig, Germany). ..

    Article Title: A multicentre registry of hospitalized patients with acute and chronic heart failure: Study design of the H 2 -registry.
    Article Snippet: .. An optical character recognition software (ABBYY, Charlotte, NC, USA) is used to facilitate PROM data capturing. ..

    Article Title: Methods and systems for artificial intelligence- assisted document annotation
    Article Snippet: .. As illustrated in FIG. 24, additional software modules (e.g., ABBYY optical character recognition, flexible document layout extraction tools, template-based document extraction tools, etc.) may interface with the AIDA application and also with the data platform to provide optical character recognition and template-based document data extraction capabilities that may be performed in parallel with the machine learning-based data extraction process. ..

    Extraction:

    Article Title: Methods and systems for artificial intelligence- assisted document annotation
    Article Snippet: .. As illustrated in FIG. 24, additional software modules (e.g., ABBYY optical character recognition, flexible document layout extraction tools, template-based document extraction tools, etc.) may interface with the AIDA application and also with the data platform to provide optical character recognition and template-based document data extraction capabilities that may be performed in parallel with the machine learning-based data extraction process. ..



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    Example output of fiducial and label detection. The blue color component is shown, with boxed connected components in yellow. The fiducial locations are indicated as cyan and magenta asterisks. Letter labels, as output from the letter <t>recognition</t> function, are shown above the image patch in magenta. Here, all labels are correct except for “N”, which is mislabeled as “M”.
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    Image Search Results


    Example output of fiducial and label detection. The blue color component is shown, with boxed connected components in yellow. The fiducial locations are indicated as cyan and magenta asterisks. Letter labels, as output from the letter recognition function, are shown above the image patch in magenta. Here, all labels are correct except for “N”, which is mislabeled as “M”.

    Journal: Proceedings of SPIE--the International Society for Optical Engineering

    Article Title: Textual fiducial detection in breast conserving surgery for a near-real time image guidance system

    doi: 10.1117/12.2550662

    Figure Lengend Snippet: Example output of fiducial and label detection. The blue color component is shown, with boxed connected components in yellow. The fiducial locations are indicated as cyan and magenta asterisks. Letter labels, as output from the letter recognition function, are shown above the image patch in magenta. Here, all labels are correct except for “N”, which is mislabeled as “M”.

    Article Snippet: Letters can be recognized with 89% accuracy using the MATLAB built in optical character recognition function, and an average of 81% of points can be accurately labeled and localized.

    Techniques:

    Examples of blue color component image patches that were fed into the optical character recognition function and mislabeled. The output label is displayed in magenta. The fiducial location, as determined by the brightest pixel in the red color channel (not shown), is indicated with a cyan and magenta asterisk.

    Journal: Proceedings of SPIE--the International Society for Optical Engineering

    Article Title: Textual fiducial detection in breast conserving surgery for a near-real time image guidance system

    doi: 10.1117/12.2550662

    Figure Lengend Snippet: Examples of blue color component image patches that were fed into the optical character recognition function and mislabeled. The output label is displayed in magenta. The fiducial location, as determined by the brightest pixel in the red color channel (not shown), is indicated with a cyan and magenta asterisk.

    Article Snippet: Letters can be recognized with 89% accuracy using the MATLAB built in optical character recognition function, and an average of 81% of points can be accurately labeled and localized.

    Techniques: