Save Screenshots as Searchable PDFs using LEADTOOLS OCR

Posted on 2019-12-04 by Nick Villalobos

Taking a screenshot is a quick and easy way to capture and share information. While Windows provides a snipping tool, it can only capture and save images. With this code, you can save the image as well as the text in the image. This text can be indexed so you can find the information later using Windows built-in search.

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Extract Embedded Images from PDF Documents Using LEADTOOLS

Posted on 2019-11-07 by Nick Villalobos

Digital images are everywhere you look. There’s no escaping them. They can be found in just about every email, they’re all over social media, and they can be embedded throughout PDFs. Some may embed images into PDFs to make the document look better or to provide visuals. Others may do this to show images for legal reasons, such as insurance adjusters.

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Integrate LEADTOOLS Computer Vision Solutions: Multimedia and Motion Detection

Posted on 2019-10-09 by Nick Villalobos

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see”. LEADTOOLS is built upon nearly 30 years of programming and patented AI algorithms dealing with the understanding of images and videos. When it comes to putting computer vision into practice, we’ve got you covered!

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Wrapping up September with .NET Conf and National Health IT Week

Posted on 2019-09-23 by Katie

This week is shaping up to be very busy and exciting as LEAD is involved with the .NET conference and National Health IT Week. Both events offer an opportunity for LEADTOOLS to be showcased in different tech fields.

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Processing Large Test Sheets and Surveys with LEADTOOLS OMR

Posted on 2019-09-18 by Nick Villalobos

Optical Mark Recognition (OMR) is an important part of forms recognition but can be surprisingly complex. On the surface, it looks easy to detect whether a box is filled or unfilled by just counting the pixels. While that may be true for the small area, how do you handle an image with hundreds of checkboxes like a multiple-choice exam sheet or survey?

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