Context-dependent substroke model for HMM-based on-line handwriting recognition

Describes context-dependent substroke hidden Markov models (HMMs)for on-line handwritten recognition of cursive Kanji and Hiragana characters. In order to tackle this problem, we have proposed the substroke HMM approach where a modeling unit "substroke" that is much smaller than a whole character is employed and each character is modeled as a concatenation of only 25 kinds of substroke HMMs. One of the drawbacks of this approach is that the recognition accuracy deteriorates in the case of scribbled characters, and characters where the shape of the substrokes varies a lot. We show that the context-dependent substroke modeling which depends on how the substroke connects to the adjacent substrokes is effective for achieving robust recognition of low quality characters, The successive state splitting algorithm which was mainly developed for speech recognition is employed to construct the context dependent substroke HMMs. Experimental results show that the correct recognition rate improved from 88% to 92% for cursive Kanji handwriting and from 90% to 98% for Hiragana handwriting

On-line Overlaid-Handwriting Recognition Based on Substroke HMMs.

This study discusses the subject of training data selection for neural networks using back propagation. We have made only one assumption that there are no overlapping of training data belonging to different classes, in other words the training data is linearly/semi-linearly separable . Training data is analyzed and the data that affect the learning process are selected based on the idea of Critical points. The proposed method is applied to a classification problem where the task is to recognize the characters A,C and B,D. The experimental results show that in case of batch mode the proposed method takes almost 1/7 of real and 1/10 of user training time required for conventional method. On the other hand in case of online mode the proposed method takes 1/3 of training epochs, 1/9 of real and 1/20 of user and 1/3 system time required for the conventional method. The classification rate of training and testing data are the same as it is with the conventional method

On-line handwriting recognition using hidden Markov models

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 84-86).by Han Shu.M.Eng

Pen pressure features for writer-independent on-line handwriting recognition based on substroke HMM

This paper discusses the use of pen pressure as a feature in writer-independent on-line handwriting recognition. We propose two kinds of features related to pen pressure: one is the pressure representing pen ups and downs in a continuous manner; the other is the time-derivative of the pressure representing the temporal pattern of the pen pressure. Combining either of them with the existing feature (velocity vector), a 3-dimensional feature is composed for character recognition. Some techniques of interpolating the pen pressure during the pen-up interval is also proposed for a pre-processing purpose. Through experimental evaluation using 1,016 elementary Kanji characters compared with the baseline performance using velocity vector only, the additional use of pen pressure improved the performance from 97.5% to 98.1% for careful writings and from 91.1% to 93.1% for cursive writings

Incorporation of relational information in feature representation for online handwriting recognition of Arabic characters

Interest in online handwriting recognition is increasing due to market demand for both improved performance and for extended supporting scripts for digital devices. Robust handwriting recognition of complex patterns of arbitrary scale, orientation and location is elusive to date because reaching a target recognition rate is not trivial for most of the applications in this field. Cursive scripts such as Arabic and Persian with complex character shapes make the recognition task even more difficult. Challenges in the discrimination capability of handwriting recognition systems depend heavily on the effectiveness of the features used to represent the data, the types of classifiers deployed and inclusive databases used for learning and recognition which cover variations in writing styles that introduce natural deformations in character shapes. This thesis aims to improve the efficiency of online recognition systems for Persian and Arabic characters by presenting new formal feature representations, algorithms, and a comprehensive database for online Arabic characters. The thesis contains the development of the first public collection of online handwritten data for the Arabic complete-shape character set. New ideas for incorporating relational information in a feature representation for this type of data are presented. The proposed techniques are computationally efficient and provide compact, yet representative, feature vectors. For the first time, a hybrid classifier is used for recognition of online Arabic complete-shape characters based on the idea of decomposing the input data into variables representing factors of the complete-shape characters and the combined use of the Bayesian network inference and support vector machines. We advocate the usefulness and practicality of the features and recognition methods with respect to the recognition of conventional metrics, such as accuracy and timeliness, as well as unconventional metrics. In particular, we evaluate a feature representation for different character class instances by its level of separation in the feature space. Our evaluation results for the available databases and for our own database of the characters' main shapes confirm a higher efficiency than previously reported techniques with respect to all metrics analyzed. For the complete-shape characters, our techniques resulted in a unique recognition efficiency comparable with the state-of-the-art results for main shape characters

Special Radical Detection by Statistical Classification for On-line Handwritten Chinese Character Recognition

International audienceThe hierarchical nature of Chinese characters has inspired radical-based recognition, but radical segmentation from characters remains a challenge. We previously proposed a radical-based approach for on-line handwritten Chinese character recognition, which incorporates character structure knowledge into integrated radical segmentation and recognition, and performs well on characters of left-right and up-down structures (non-special structures). In this paper, we propose a statistical-classification-based method for detecting special radicals from special-structure characters. We design 19 binary classifiers for classifying candidate radicals (groups of strokes) hypothesized from the input character. Characters with special radicals detected are recognized using special-structure models, while those without special radicals are recognized using the models for non-special structures. We applied the recognition framework to 6,763 character classes, and achieved promising recognition performance in experiments

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject