Vigle: A Visual Graphical Learning Module on Optical Character Recognition

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Rao_Visual Graphical Learning Module_ID_05-12-19.pdf (2.86 MB)

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2019-04-18

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Ho, Curtis

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TCC Conference, 2019

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Abstract

Students of the Arts and Humanities use OCR to convert scanned images of old text (pre-1800 AD). They need to know how digital text is extracted from the scanned image. Thanks to cell phones and images captured with them, understanding this is useful for everybody. The processing steps employed by a typical OCR software are, in order, Binarization, Deskew, Segmentation, Character Segmentation and Character Recognition. In this research project, a standalone asynchronous visual graphical learning environment (VIGLE) on Optical Character Recognition (OCR) was developed. Constructivist learning strategy was employed. The learning module was integrated into a website that works on mobile. The project attempts to generalize the instruction so that it is useful for everybody. Latest web technology was used for the implementation to achieve one stop interface, browser compatibility, responsive window sizing and interactive visual content. Binarization, Deskew and Segmentation modules were implemented in the time available. The VIGLE consists of a graphical representation and a visual interface to the lessons. Results show that the participants found both the graphical representation and the visual interface helpful. They found the incomplete learning module on OCR at best moderately useful in helping them digitize text.

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Keywords

Graphical and visual interface, interactive instruction, constructivist learning, learning environment, optical character recognition

Citation

Rao, Umesh (2019, April 18). ViGLe: A Visual Graphical Learning Module on Optical Character Recognition. Powerpoint presented at the 24th annual Technology, Colleges and Community Worldwide Online Conference.

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http://hdl.handle.net/10125/61929

Extent

49 pages

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Please see the Creative Commons license for this item.
Attribution-ShareAlike 3.0 United States

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LTEC 690, Spring 2019
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