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VeryUtils VeryUtils Digit OCR Solution 2.7
Business :: Office Suites & Tools
(Click to Enlarge)
VeryUtils Digit OCR Solution for Handwritten and Printed Digit Recognition. VeryUtils Digit OCR is a solution to extract handwritten & printed numbers from various kinds of documents, images and photos. For example, If you have an image that contains a set of handwritten digits, you need to extract each digit from the image, this Digit OCR software will be the right choice for you. The idea of converting written or printed text into digital text is called OCR (Optical Character Recognition). Detecting handwritten characters is a very difficult work. Today's OCR software and developed algorithms can not reach 100% accuracy, even a real person cannot always recognize what is written. In general, an OCR application works best when dealing with some limited works or domains. For example, it's possible to recognize the postcodes, license numbers, city names, credit card numbers, passport numbers, etc. Many handwritten text detection systems are built following traditional image processing methods and work well with printed text, but if they are used for handwritten text recognition in images, it can get unexpected results and the recognition quality is poor. Our image processing engineers have developed a handwritten Text & Digit detection prototype that applies a custom algorithm that provides high level of accuracy, and eliminate most of OCR limitations. We have a demo version of VeryUtils Digit OCR software for your evaluation, https://veryutils.com/dl.php/DigitOCR.zip You may evaluate VeryUtils Digit OCR software by following steps, 1. Download it to your system, unzip to a folder, 2. Run a CMD window, you can run following command line to train VeryUtils Digit OCR engine first, but if you want to use already trained data, you can skip this step, digit_recognizer.exe --mode train --file "digit-template.png" 3. After you train the OCR engine, you may test the OCR engine by following command lines,
Specifications:
Version: 2.7 |
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