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Publication Date


First Advisor

Nicholas R. Howe and Gwen Spencer

Document Type

Honors Project

Degree Name

Bachelor of Arts


Computer Science


Word spotting, Graph-based matching, Inkball models, Part-structured inkball models, Segmentation-free, Handwriting recognition, Document analysis, Word recognition-Mathematical models, Statistical matching, Paleography


Handwritten historical manuscripts traditionally have been manually transcribed for the purpose of preservation and dissemination. Part-structured Inkball models provide a potentially segmentation-free word spotting method that automates query searches in large collections of texts. However, the existing technique considers the graphical structure of words only to a limited extent. This thesis proposes a new method to measure the similarity of two inkball models. We first propose a bidirectional match between two graphs. Then we introduce two new measures to capture many-to-one matches of nodes and the structural differences between graphs. Tested on the standard George Washington 20 dataset, the method shows modest improvement in comparison to the original technique.


2018 Ji Won Chung. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.




vi, 43 pages : color illustrations. Includes bibliographical references (pages 42-43)