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optical character recognition function  (MathWorks Inc)


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    MathWorks Inc optical character recognition function
    Optical Character Recognition Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 400 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/optical+character+recognition+function/Computer+Vision+Toolbox/pm38311108-65-18-28
    Average 96 stars, based on 400 article reviews
    optical character recognition function - by Bioz Stars, 2026-09
    96/100 stars

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    Related Articles

    other:

    Article Title: Effect of Nonoverlapping Visual Field Defects on Vision-related Quality of Life in Glaucoma.
    Article Snippet: For this purpose, pointwise total deviation (TD) values were extracted from the HFA printouts by means of the Optical Character Recognition function from the Computer Vision Toolbox in MATLAB (version R2021a; Mathworks), using a training data set made specifically for this purpose.

    Software:

    Article Title: Small Text on Product Labels Poses a Special Challenge for Emerging Presbyopes
    Article Snippet: METHODS: Geometrical optics was used to determine the impact of changing viewing distance, accommodation, and pupil size on retinal blur size.. We photographed 261 consumer product labels in grocery, personal care, and nonprescription (over-the-counter) drug categories and used character recognition software to identify and size 255,298 printed letters.. We computed the impact of viewing distance on the ratio of blur to letter detail and used published blur ratios of ≤4 and ≤2 to identify the conditions that allowed for letter recognition and proficient reading, respectively.



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    built in optical character recognition function  (MathWorks Inc)
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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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    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: