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首页> 外文期刊>International journal of human-computer studies >Human performance modeling in temporary segmentation Chinese character handwriting recognizers
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Human performance modeling in temporary segmentation Chinese character handwriting recognizers

机译:临时分割汉字笔迹识别器中的人类行为建模

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Human performance in Chinese character handwriting recognizers is critical to the satisfaction and acceptance of their users. Based on Teal's [CHI'92 (1992) p. 295] interactive model, a static model describing the independent factors in determining the task completion time was set up with a simple mathematical inference; in addition, a dynamic model describing these factors' direct and indirect causal relationship was established by the path analytic method. Results in Experiment 1 indicated that both the static model and the dynamic model could fit observed task completion time satisfactorily with minor modifications. In addition, with users' average writing time around 1500ms for each frequently used character, it was found that the user's performance was impaired significantly when segmentation time was longer than 1040 ms. An integrated model was devised after combining the static and dynamic models. Experiment 2 testified the integrated model in another handwriting recognizer and found that it could still fit human performance data with users in three different training conditions. Implications of the integrated model are that: (1) when recognition accuracy and number of inputting characters are constant, the weights of average writing time for each character, segmentation time, recognition time in determining task completion time are equal but bigger than the weight of the repairing time; (2) when the repairing time, average writing time for each character, segmentation time and recognition time are constant, there is an inverse model between task completion time and recognition accuracy; when recognition accuracy is from 50% to 93%, every 1% increase of recognition accuracy will reduce task completion time from 1989 to 1915ms; when recognition accuracy increases from 94% to 100%, every 1% increase of recognition accuracy will reduce task completion time from 1895 to 1392 ms. Guidelines in designing these recognizers were given based on these implications. (C) 2003 Elsevier Science Ltd. All rights reserved. [References: 43]
机译:汉字手写识别器的人类性能对于用户的满意度和接受度至关重要。基于Teal的[CHI'92(1992)p。 [295]交互模型,通过简单的数学推论建立了一个静态模型,该模型描述了确定任务完成时间的独立因素;此外,通过路径分析方法建立了描述这些因素直接和间接因果关系的动态模型。实验1的结果表明,静态模型和动态模型都可以通过较小的修改而令人满意地适合观察的任务完成时间。此外,对于每个常用字符,用户的平均书写时间约为1500ms,发现当分割时间超过1040 ms时,用户的性能将受到极大损害。在将静态和动态模型结合之后,设计出一个集成模型。实验2在另一个手写识别器中对该集成模型进行了验证,发现该模型仍可以在三种不同的训练条件下与用户拟合人类绩效数据。集成模型的含义是:(1)在识别精度和输入字符数不变的情况下,确定任务完成时间时每个字符的平均书写时间,分割时间,识别时间的权重相等,但大于权重。维修时间; (2)当修复时间,每个字符的平均书写时间,分割时间和识别时间恒定时,任务完成时间与识别精度之间存在反模型;当识别精度从50%到93%时,识别精度每提高1%,任务完成时间就会从1989年减少到1915毫秒;当识别精度从94%提高到100%时,识别精度每提高1%,任务完成时间将从18​​95毫秒减少到1392毫秒。基于这些含义,给出了设计这些识别器的指南。 (C)2003 Elsevier ScienceLtd。保留所有权利。 [参考:43]

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