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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. [1]
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 ...
Any printed font: Text, ALTO, hOCR, [19] PDF, others with different user interfaces [20] or the API: Created by Hewlett-Packard; under further development by Google [21] Name Founded year Latest stable version Release year License Online Windows Mac OS X Linux BSD Android iOS Programming language SDK? Languages Fonts Output Formats Notes
OCR-A is a font issued in 1966 [2] and first implemented in 1968. [3] A special font was needed in the early days of computer optical character recognition, when there was a need for a font that could be recognized not only by the computers of that day, but also by humans. [4] OCR-A uses simple, thick strokes to form recognizable characters. [5]
CuneiForm is a system developed for transforming the electronic copies of paper documents and image files into an editable form without changing the structure and the original document fonts in automatic or semi-automatic mode. The system includes two components for single and batch processing of electronic documents.
Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices.
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 and position. The recognition of scene text is further complicated sometimes by non-uniform illumination and focus.
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]