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Core

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We are constantly looking for ways to improve Datasnipper, and we believe that the best way to do this is by listening to our users. We would love to hear your ideas and suggestions for how we can make the product even better.

Whether you have a feature request or a general suggestion, we want to hear it all. Your feedback is valuable to us, and it will help us prioritize our roadmap and make sure that we are building the right things for our users.
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Core

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4 results found

  1. Enhance OCR accuracy to distinguish currency symbol and number

    Currently, in Document Matching, Form Extraction, etc., a currency symbol such as "￥" is recognized as a number"1" or "7" when extracting the amount of money, especially if it is handwritten.
    Ideally, improve the accuracy of OCR to distinguish between currency symbol and number.

    17 votes

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    Under Review  ·  1 comment  ·  OCR (text recognition)  ·  Admin →
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  2. OCR Health Score

    Implement a confidence scoring system to provide users with insights into the accuracy of OCR-extracted text. I envision this feature offering both document-level and word/number-level confidence scores, empowering users to evaluate the reliability of the extracted data and make informed decisions.

    Granular Confidence Levels: Clearly define confidence score ranges (e.g., high, medium, low) and provide corresponding probability values for better interpretability.
    Visual Indicators: Incorporate visual cues (e.g., color-coding, icons) to quickly convey confidence levels, enhancing user experience.

    5 votes

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    Under Review  ·  0 comments  ·  OCR (text recognition)  ·  Admin →
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  3. Export OCR PDF file >> keep OCR

    If I, after running OCR on a PDF, export the pdf to my computer, the text is unrecognized and I can't search it anymore using for example Acrobat. It would be nice and usefull if a PDF that was text recognised, stays that way after export.

    1 vote

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    Under Review  ·  0 comments  ·  OCR (text recognition)  ·  Admin →
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  4. Run OCR only for the portions of that document which do not contain computer generated text

    OCR currently overwrites all computer generated text, sometimes with worse results. OCR may be required if a portion of the document contains computer generated text but a portion does not. In these instances it would be ideal to only run use the OCR to recognize the text that is not yet included in the text layer of the file.

    3 votes

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    Under Review  ·  0 comments  ·  OCR (text recognition)  ·  Admin →
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