Optical Character Recognition For .NET
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Aspose.OCR for .NET is a powerful yet easy-to-use and cost-effective API for optical character recognition. With it, you can add OCR functionality to your .NET applications in less than 5 lines of code without worrying about complex math, neural networks, and other technical details. Our experience in machine learning technologies and years of development resulted in an OCR engine with superior speed and accuracy that supports 26 languages based on Latin and Cyrillic scrips as well as Chinese. OCR API can recognize scanned images, smartphone photos, screenshots, areas of images, and scanned PDFs and return results in the most popular document and data exchange formats. Various pre-processing filters allow you to recognize rotated, skewed and noisy images. Recognition performance and system load can be further improved by transferring of resource intensive computational tasks to the GPU.
Use the OCR client library to read printed and handwritten text from a remote image. The OCR service can read visible text in an image and convert it to a character stream. For more information on text recognition, see the Optical character recognition (OCR) overview. The code in this section uses the latest Computer Vision SDK release for Read 3.0.
Use the OCR client library to read printed and handwritten text from a remote image. The OCR service can read visible text in an image and convert it to a character stream. For more information on text recognition, see the Optical character recognition (OCR) overview.
Use the Optical character recognition client library to read printed and handwritten text with the Read API. The OCR service can read visible text in an image and convert it to a character stream. For more information on text recognition, see the Optical character recognition (OCR) overview.
Pattern matching works by isolating a character image, called a glyph, and comparing it with a similarly stored glyph. Pattern recognition works only if the stored glyph has a similar font and scale to the input glyph. This method works well with scanned images of documents that have been typed in a known font.
A simple OCR engine works by storing many different font and text image patterns as templates. The OCR software uses pattern-matching algorithms to compare text images, character by character, to its internal database. If the system matches the text word by word, it is called optical word recognition. This solution has limitations because there are virtually unlimited font and handwriting styles, and every single type cannot be captured and stored in the database.
Modern OCR systems use intelligent character recognition (ICR) technology to read the text in the same way humans do. They use advanced methods that train machines to behave like humans by using machine learning software. A machine learning system called a neural network analyzes the text over many levels, processing the image repeatedly. It looks for different image attributes, such as curves, lines, intersections, and loops, and combines the results of all these different levels of analysis to get the final result. Even though ICR typically processes the images one character at a time, the process is fast, with results obtained in seconds.
Optical character recognition (OCR) is sometimes referred to as text recognition. An OCR program extracts and repurposes data from scanned documents, camera images and image-only pdfs. OCR software singles out letters on the image, puts them into words and then puts the words into sentences, thus enabling access to and editing of the original content. It also eliminates the need for manual data entry.
OCR software can take advantage of artificial intelligence (AI) to implement more advanced methods of intelligent character recognition (ICR), like identifying languages or styles of handwriting. The process of OCR is most commonly used to turn hard copy legal or historical documents into pdf documents so that users can edit, format and search the documents as if created with a word processor. 2b1af7f3a8