Date of Submission
Academic Programs and Concentrations
Computer Science; Mathematics
Project Advisor 1
Project Advisor 2
In the past decade, handwritten Chinese character recognition has received renewed interest with the emergence of touch screen devices. Other popular applications include on-line Chinese character dictionary look-up and visual translation in mobile phone applications. Due to the complex structure of Chinese characters, this classification task is not exactly an easy one, as it involves knowledge from mathematics, computer science, and linguistics.
Given a large image database of handwritten character data, the goal of my senior project is to use Non-Negative Matrix Factorization (NMF), a recent method for finding a suitable representation (parts-based representation) of image data, to detect specific sub-components in Chinese characters. NMF has only been applied to typed (printed) Chinese characters in different fonts. This project focuses specifically on how well NMF works on handwritten characters. In addition, research in Chinese character classification has mainly been done using holistic approaches - treating each character as an inseparable unit. By using NMF, this project takes a different approach by focusing on a more specific problem in Chinese character classification: radical (sub-component) detection.
Finally, a possible application of radical detection will be proposed. This interactive application can potentially help Chinese language learners better recognize characters by radicals.
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Shuai, Xiangying, "Radical Recognition in Off-Line Handwritten Chinese Characters Using Non-Negative Matrix Factorization" (2016). Senior Projects Spring 2016. 367.
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