Generation of hierarchical dictionary for stroke-order free Kanji handwriting recognition based on substroke HMM

Abstract

This paper describes a method of generating a Kanji hierarchical structured dictionary for stroke-number and stroke-order free handwriting recognition based on substroke HMM. In stroke-based methods, a large number of stroke-order variations can be easily expressed by just adding different stroke sequences to the dictionary and it is not necessary to train new reference patterns. The hierarchical structured dictionary has an advantage that thousands of stroke-order variations of Kanji characters can be produced using a small number of stroke-order rules defining Kanji parts. Moreover, the recognition speed is fast since common sequences are shared in a substroke network, even if the total number of stroke-order combinations becomes enormous practically. In experiments, 300 different stroke-order rules of Kanji parts were statistically chosen by using 60 writers' handwritings of 1,016 educational Kanji characters. By adding these new stroke-order rules to the dictionary, about 9,000 variations of different stroke-orders were generated for 2,965 JIS 1st level Kanji characters. As a result, we successfully improved the recognition accuracy from 82.6% to 90.2% for stroke-order free handwritings.

Allograph modeling for online handwritten characters in devanagari using constrained stroke clustering

“…Moreover, a new way of writing a character, differing only in stroke order, can easily be supported by simply adding the corresponding stroke sequence in the dictionary [Nakai et al 2001], thereby circumventing the need for more samples and fresh training. The sharing of stroke labels across character classes also allows the design of stroke-based recognition strategies [Nakai et al 2001[Nakai et al , 2003Yamasaki 1999], and enables the realization of certain incremental handwriting-based text entry methods (e.g., QuickStroke [Matic et al 2002]). …”

“…Nakai et al [2001] identified 25 substrokes in Kanji characters based on their direction and length, which may be used to express any Kanji character. While a hierarchical dictionary consisting of substrokes, strokes, radicals and characters is manually built for recognition of Kanji characters featuring known stroke orders, new stroke orders are learnt by generating permutations of the known stroke orders in the dictionary and matching with the samples in the training data [Nakai et al 2003]. Yamasaki [1999] proposes a two-stage stroke clustering approach for Japanese character recognition.…”

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.…”