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Content Detection in Handwritten Documents

Abstract Handwritten documents have gained popularity in various domains including education and business. A key task in analyzing a complex document is to distinguish between various content types such as text, math, graphics, tables and so on. For example, one such aspect could be a region on the document with a mathematical expression; in this case, the label would be math. This differentiation facilitates the performance of specific recognition tasks depending on the content type. We hypothesize that the recognition accuracy of the subsequent tasks such as textual, math, and shape recognition will increase, further leading to a better analysis of the document.

Content detection on handwritten documents assigns a particular class to a homogeneou... (more)
Created Date 2018
Contributor Faizaan, Shaik Mohammed (Author) / VanLehn, Kurt (Advisor) / Cheema, Salman Shaukat (Advisor) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Subject Computer science / Convolutional Neural Networks / Handwritten documents / Machine learning / Object detection
Type Masters Thesis
Extent 54 pages
Language English
Note Masters Thesis Computer Science 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis