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.