• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.814-820
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• 2009

The arrangement of the general 12-button cellular phone keypad is unified with one or two kind. In general, the several alphabets are arranged in one key. When inputting an English, countries which do not speak English use multi-tap method. We can input next alphabet by pressing key repeatedly. We address the problem of finding multi-tap-based keypad designs which minimize the number of key presses for various case. Genetic algorithms is proposed method for optimal keypad designs. We are to maintain the number of keys, which is ranged from 8 to 12. We also show experimental using non-alphabetically-constrained and alphabetically-constrained arrangement, respectively. Finally, we give improved keypad designs.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.5
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• pp.43-51
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• 2012

Smart phones provide variety of functions like world processing, chatting, web browsing, etc. for users. Therefore, efficiency of the keypad that are used in text entry are becoming more important. But, in traditional english mobile keypad, text entry speed is not efficient because simple placement of characters in accordance with the order of characters. In this paper, we propose the improved english keypad for smart phone using the frequency information of english characters and bigram. And, we verified that the proposed keypad is more efficient than other keypads through the experiment.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.81-89
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• 2021

This study proposes a text interface for support of language-based educational contents in a mobile platform environment. The proposed interface utilizes deep learning as an input structure to write words through handwriting. Based on GUI (Graphical User Interface) using buttons and menus of mobile platform contents and input methods such as screen touch, click, and drag, we design a text interface that can directly input and process handwriting from the user. It uses the EMNIST (Extended Modified National Institute of Standards and Technology database) dataset and a trained CNN (Convolutional Neural Network) to classify and combine alphabetic texts to complete words. Finally, we conduct experiments to analyze the learning support effect of the interface proposed by directly producing English word education contents and to compare satisfaction. We compared the ability to learn English words presented by users who have experienced the existing keypad-type interface and the proposed handwriting-based text interface in the same educational environment, and we analyzed the overall satisfaction in the process of writing words by manipulating the interface.