Trie-Lexicon-Driven Recognition for On-line Handwritten Japanese Disease Names Using a Time-Synchronous Method

This paper describes a lexicon-driven approach to on-line handwritten Japanese disease name recognition using a time-synchronous method. A trie lexicon is constructed from a disease name database containing 21,713 disease name phrases. It expands the search space using a time-synchronous method and applies the beam search strategy to search into a segmentation candidate lattice constructed based on primitive segments. This method restricts the character categories for recognizing each character candidate pattern from the trie lexicon of disease names and preceding paths during path search in the segmentation candidate lattice, and selects an optimal disease name from the disease name database as recognition result. The experimental results demonstrate the effectiveness of our proposed method, which improves character recognition rate from 94.56% to 99.97% compared with a general-purpose Japanese text recognizer and speeds up recognition time as 4.3 times faster as the general recognizer.