Yuto Yamaji

Online Handwritten Stroke Type Determination Using Descriptors Based on Spatially and Temporally Neighboring Strokes

We investigate the task of single-stroke classification into one of three classes (text, figure, or table rule lines). Individual strokes form handwriting structures such as text lines, figures, and tables in combination with peripheral strokes. To classify strokes using local contexts of neighborhood strokes, we propose a composite descriptor that represents in detail the relation between individual strokes and temporal and spatial neighborhood strokes. Evaluation of online handwritten documents written in English and in Japanese indicate that the proposed method more accurately classifies strokes than does the conventional method that employs shape-related features of a single stroke.

Fast and Memory Efficient Online Handwritten Strokes Retrieval Using Binary Descriptor

We propose a fast, memory-efficient online handwriting search method that uses handwritten strokes as a query and finds matches from among handwritten documents. The proposed method is language-independent, so not only words but also figures and symbols can be queried. We introduce a compact binary descriptor to lower computational resource load. A metric learning method enables derivation of a discriminative binary descriptor from directional densities of handwritten strokes. Experiments indicate that the proposed method is faster, more memory efficient, and exhibits more accurate search performance than a conventional method that employs directional densities. For 200 handwritten documents, the proposed method completed query searches within 1 s using a 1.3 GHz Tegra 3 CPU.