Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

'Online recognition of Chinese characters: the state-of-the-art

A method of receiving input from a user includes sensing a first trajectory of a center of mass of a hand of the user during a gesture made by the hand. A second trajectory of a finger tip of the hand of the user during the gesture made by the hand is also sensed. An alphanumeric character represented by the gesture made by the hand is determined dependent upon both the first trajectory and the second trajectory.

Robust video-based handwriting and gesture recognition for in-car applications

Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts In this paper, we address this problem specifically for two major Indic scripts-Devanagari and Tamil In contrast to previous approaches, the techniques we propose are largely data driven and script independent We propose two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free The lexicon-driven technique models each word in the lexicon as a sequence of symbol HMMs according to a standard symbol writing order derived from the phonetic representation The lexicon-free technique uses a novel Bag-of-Symbols representation of the handwritten word that is independent of symbol order and allows rapid pruning of the lexicon On handwritten Devanagari word samples featuring both standard and nonstandard symbol writing orders, a combination of lexicon-driven and lexicon-free recognizers significantly outperforms either of them used in isolation In contrast, most Tamil word samples feature the standard symbol order, and the lexicon-driven recognizer outperforms the lexicon free one as well as their combination The best recognition accuracies obtained for 20,000 word lexicons are 8713 percent for Devanagari when the two recognizers are combined, and 918 percent for Tamil using the lexicon-driven technique

HMM-Based Lexicon-Driven and Lexicon-Free Word Recognition for Online Handwritten Indic Scripts

This paper compares the current state of the art in online Japanese character recognition with techniques in western handwriting recognition. It discusses important developments in preprocessing, classification, and postprocessing for Japanese character recognition in recent years and relates them to the developments in western handwriting recognition. Comparing eastern and western handwriting recognition techniques allows learning from very different approaches and understanding the underlying common foundations of handwriting recognition. This is very important when it comes to developing compact modules for integrated systems supporting many writing systems capable of recognizing multilanguage documents.

The state of the art in Japanese online handwriting recognition compared to techniques in western handwriting recognition

We investigate a new approach for online handwritten shape recognition. Interesting features of this approach include learning without manual tuning, learning from very few training samples, incremental learning of characters, and adaptation to the user-specific needs. The proposed system can deal with two-dimensional graphical shapes such as Latin and Asian characters, command gestures, symbols, small drawings, and geometric shapes. It can be used as a building block for a series of recognition tasks with many applications

Online Handwritten Shape Recognition Using Segmental Hidden Markov Models

A new method is proposed for online handwriting recognition of Kanji characters. The method employs substroke HMM 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 HMM, a character structure dictionary and a decoder. The present approach has the following advantages over the conventional methods that employ whole character HMM. 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.

/pdf/substroke-approach-to-hmm-based-on-line-kanji-handwriting-4zrac81ley.pdf

N. Akira

Papers

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