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Video of the process of scanning and real-time optical character recognition (OCR) with a portable scanner. 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 ...
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.
Intelligent character recognition (ICR) is used to extract handwritten text from images.It is a more sophisticated type of OCR technology that recognizes different handwriting styles and fonts to intelligently interpret data on forms and physical documents.
GOCR claims it can handle single-column sans-serif fonts of 20–60 pixels in height. It reports trouble with serif fonts, overlapping characters, handwritten text, heterogeneous fonts, noisy images, large angles of skew, and text in anything other than a Latin alphabet.
Sample images from MNIST test dataset. The MNIST database (Modified National Institute of Standards and Technology database [1]) is a large database of handwritten digits that is commonly used for training various image processing systems. [2] [3] The database is also widely used for training and testing in the field of machine learning.
Images, text Handwriting recognition, classification 1998 [147] E. Alpaydin et al. Pen-Based Recognition of Handwritten Digits Dataset Handwritten digits on electronic pen-tablet. Feature vectors extracted to be uniformly spaced. 10,992 Images, text Handwriting recognition, classification 1998 [148] [149] E. Alpaydin et al.