Abstract

Extracting strokes from handwriting in historical documents provides high-level features for the challenging problem of handwriting recognition. Such handwriting often contains noise, faint or incomplete strokes, strokes with gaps, overlapping ascenders and descenders and competing lines when embedded in a table or form, making it unsuitable for local line following algorithms or associated binarization schemes. We introduce Intelligent Pen for piece-wise optimal stroke extraction. Extracted strokes are stitched together to provide a complete trace of the handwriting. Intelligent Pen formulates stroke extraction as a set of piece-wise optimal paths, extracted and assembled in cost order. As such, Intelligent Pen is robust to noise, gaps, faint handwriting and even competing lines and strokes. Intelligent Pen traces compare closely with the shape as well as the order in which the handwriting was written. A quantitative comparison with an ICDAR handwritten stroke data set shows Intelligent Pen traces to be within 0.78 pixels (mean difference) of the manually created strokes.

Degree

MS

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2016-05-01

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd8527

Keywords

Handwriting, Stroke Extraction, Document Processing

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