On-line Overlaid-Handwriting Recognition Based on Substroke HMMs

This paper proposes a novel handwriting recognition interface for wearable computing where users write characters continuously without pauses on a small single writing box. Since characters are written on the same writing area, they are overlaid with each other. Therefore the task is regarded as a special case of the continuous character recognition problem. In contrast to the conventional continuous character recognition problem, location information of strokes does not help very much in the proposed framework. To tackle the problem, substroke based hidden Markov models (HMMs) and a stochastic bigram language model are employed. Preliminary experiments were carried out on a dataset of 578 handwriting sequences with a character bigram consisting of 1,016 Japanese educational Kanji and 71 Hiragana characters. The proposed method demonstrated promising performance with 69.2% of handwriting sequences beeing correctly recognized when different stroke order was permitted, and the rate was ...

• Type: Conference
• Year: 2003
• Where: ICDAR
• Authors: Hiroshi Shimodaira, Takashi Sudo, Mitsuru Nakai, Shigeki Sagayama
• Keywords: Character Recognition Problem, Continuous Character Recognition, Document Analysis, ICDAR 2003, Users Write Characters