3 research outputs found
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
An Open Source Testing Tool for Evaluating Handwriting Input Methods
This paper presents an open source tool for testing the recognition accuracy
of Chinese handwriting input methods. The tool consists of two modules, namely
the PC and Android mobile client. The PC client reads handwritten samples in
the computer, and transfers them individually to the Android client in
accordance with the socket communication protocol. After the Android client
receives the data, it simulates the handwriting on screen of client device, and
triggers the corresponding handwriting recognition method. The recognition
accuracy is recorded by the Android client. We present the design principles
and describe the implementation of the test platform. We construct several test
datasets for evaluating different handwriting recognition systems, and conduct
an objective and comprehensive test using six Chinese handwriting input methods
with five datasets. The test results for the recognition accuracy are then
compared and analyzed.Comment: 5 pages, 3 figures, 11 tables. Accepted to appear at ICDAR 201
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 improved up to 88.0% when characters were written with fixed stroke order