An Open Source Testing Tool for Evaluating Handwriting Input Methods
Liquan Qiu, Lianwen Jin, Ruifen Dai, Yuxiang Zhang, Lei Li

TL;DR
This paper introduces an open source testing platform for evaluating Chinese handwriting input methods, enabling systematic accuracy assessment across multiple datasets and input systems.
Contribution
It provides a novel, comprehensive testing tool with PC and Android modules for objective evaluation of handwriting recognition accuracy.
Findings
Test datasets for Chinese handwriting recognition
Evaluation of six input methods across five datasets
Analysis of recognition accuracy results
Abstract
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…
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Taxonomy
TopicsHand Gesture Recognition Systems · Handwritten Text Recognition Techniques
