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optical character‐recognition software  (Clinical DataFax Systems Inc)

 
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    Structured Review

    Clinical DataFax Systems Inc optical character‐recognition software
    Optical Character‐Recognition Software, supplied by Clinical DataFax Systems 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/pmc06587527-60-15-39
    Average 90 stars, based on 1 article reviews
    optical character‐recognition software - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Functional Assay:

    Article Title: Development of a patient‐reported outcomes symptom measure for patients with nontransfusion‐dependent thalassemia (NTDT‐PRO © )
    Article Snippet: Quantitative data (sociodemographic and clinical data, 36‐Item Short Form Health Survey version 2 [SF‐36v2], and Functional Assessment of Cancer Therapy‐Anemia [FACT‐An]) responses were stored in a database that was developed, tested, and validated using DataFax (an optical character‐recognition software; Clinical DataFax Systems, Inc., Hamilton, ON, Canada) and reviewed by project scientific staff.

    Software:

    Article Title: Development of a patient‐reported outcomes symptom measure for patients with nontransfusion‐dependent thalassemia (NTDT‐PRO © )
    Article Snippet: Quantitative data (sociodemographic and clinical data, 36‐Item Short Form Health Survey version 2 [SF‐36v2], and Functional Assessment of Cancer Therapy‐Anemia [FACT‐An]) responses were stored in a database that was developed, tested, and validated using DataFax (an optical character‐recognition software; Clinical DataFax Systems, Inc., Hamilton, ON, Canada) and reviewed by project scientific staff.



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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: