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Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene photo (for example the text on signs and billboards in a landscape photo) or from subtitle text ...
Optical character recognition (OCR) is commonly considered to apply to any recognition technique that reads machine printed text. An example of a traditional OCR use case would be to translate the characters from an image of a printed document, such as a book page, newspaper clipping, or legal contract, into a separate file that could be ...
Image translation is the machine translation of images of printed text (posters, banners, menus, screenshots etc.). This is done by applying optical character recognition (OCR) technology to an image to extract any text contained in the image, and then have this text translated into a language of their choice, and the applying digital image processing on the original image to get the ...
The image displays the coach category in text format. We can observe that the coach belongs to Sleeper category. The detection and recognition of scene text from camera captured images are computer vision tasks which became important after smart phones with good cameras became ubiquitous. The text in scene images varies in shape, font, colour ...
Images, text Face recognition 2014 [99] [100] H. Ng et al. BioID Face Database Images of faces with eye positions marked. Manually set eye positions. 1521 Images, text Face recognition 2001 [101] [102] BioID Skin Segmentation Dataset Randomly sampled color values from face images. B, G, R, values extracted. 245,057 Text Segmentation ...
Offline handwriting recognition involves the automatic conversion of text in an image into letter codes that are usable within computer and text-processing applications. The data obtained by this form is regarded as a static representation of handwriting.
A 2016 analysis of the accuracy and reliability of the OCR packages Google Docs OCR, Tesseract, ABBYY FineReader, and Transym, employing a dataset including 1227 images from 15 different categories concluded Google Docs OCR and ABBYY to be performing better than others.
Other typical applications of pattern recognition techniques are automatic speech recognition, speaker identification, classification of text into several categories (e.g., spam or non-spam email messages), the automatic recognition of handwriting on postal envelopes, automatic recognition of images of human faces, or handwriting image ...
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