Substroke approach to HMM-based on-line Kanji handwriting recognition

Abstract

A new method is proposed for on-line handwriting recognition of Kanji characters. The method employs substroke HMMs as minimum units to constitute Japanese Kanji characters and utilizes the direction of pen motion. The main motivation is to fully utilize the continuous speech recognition algorithm by relating sentence speech to Kanji character, phonemes to substrokes, and grammar to Kanji structure. The proposed system consists input feature analysis, substroke HMMs, a character structure dictionary and a decoder. The present approach has the following advantages over the conventional methods that employ whole character HMMs. 1) Much smaller memory requirement for dictionary and models. 2) Fast recognition by employing efficient substroke network search. 3) Capability of recognizing characters not included in the training data if defined as a sequence of substrokes in the dictionary. 4) Capability of recognizing characters written by various different stroke orders with multiple definitions per one character in the dictionary. 5) Easiness in HMM adaptation to the user with a few sample character data.

Online Handwritten Chinese/Japanese Character Recognition

“…HMMs were first described in a series of statistical papers [51] and applied to speech recognition [52][53] in the middle of the 1970s. Then, they were applied widely to online handwriting [12][13][14][15][16][17][18][19][20][21] and offline word recognition [32][33][34][35][36][37][38].…”

“…For online recognition, structural features are often employed with hidden Markov models (HMMs) [12][13][14][15][16][17][18][19][20][21] or Markov random field (MRF) [29,30]. However, since the un-structural features are easily extracted from an online handwritten pattern by discarding temporal and structural information, we can apply the un-structural method as well.…”

“…Un-structural methods are also called statistical methods [2] or feature matching methods [3]. Structural methods are based on stroke analysis and use structural features such as sampling points, line segments and/or strokes for offline recognition [3][4][5] and for online recognition [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Un-structural methods use un-structural features such as directional features, gradient histogram features and projection features such as those for offline [22][23][24] and online recognition [25,26], which eventually achieves stroke-order independence.…”

Hierarchical Algorithm for Hidden Markov Model

“…A Hidden Markov Model (HMM) is a doubly stochastic process, one of whose components is an unobservable Markov chain; it is used extensively in pattern recognition, speech recognition [1,2], Handwriting recognition [3,4,5], computational biology [6], Machine translation [7]. During the use of HMMs we are led to treat three fundamental problems: Evaluation, decoding and learning [8].…”

Statistical Deformation Model for Handwritten Character Recognition

“…Thus, if a model learns the deformations of a certain category, it can represent the category-dependent deformation characteristics. Hidden Markov model (HMM) is a popular statistical model for handwritten characters (e.g., (Cho et al, 1995;Hu et al, 1996;Kuo & Agazzi, 1994;Nag et al, 1986;Nakai et al, 2001;Park & Lee, 1998)). HMM has not only a solid stochastic background and but also a well-established learning scheme.…”