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International Conference on Human-Computer Interaction

HCII 2022: Augmented Cognition pp 58–68Cite as

Using Augmented Cognition to Examine Differences in Online Handwriting Recognition for Native and Non-native Writers

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13310))

Abstract

The handwritten recognition (HWR) is a complex task with variety of challenges associated with natural language, variety in the styles of writing, variety and nuances of alphabets etc. The core research in handwritten recognition focuses around Latin alphabet and corresponding languages. However, differences between the languages using Latin as their main script are still major: from changed letter frequencies to additional letters. Additionally, handwriting practices and styles are not developed consistently within the same language; for example - cursive vs print calligraphy. As a result of globalization estimated 50% of world’s population speaks second language [1]. Researching characteristics of non-native handwriting has been done by various educational and second language research purposes but remains largely unaddressed in the context of augmented cognition using online handwritten recognition. We researched differences and similarities of online handwriting between native and non-native speakers of English, Georgian, Chinese and Korean speakers. We have also examined related research for Arabic, Italian and Malay handwritings. As a result, we have identified key characteristics of non-native speakers’ distinguishing from the native ones. In addition, we have identified differences based on writers’ individual maturity with the second language.

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Acknowledgement

This material is based upon work supported by the National Science Foundation (NSF) under Grant No. 1662487. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

We would like to express our gratitude to the volunteered participants for contributing to the development of the handwriting dataset.

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Authors and Affiliations

  1. University of Hawaii at Manoa, Honolulu, HI, 96822, USA

    Mariam Doliashvili, Michael-Brian C. Ogawa & Martha E. Crosby

Authors
  1. Mariam Doliashvili
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  2. Michael-Brian C. Ogawa
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  3. Martha E. Crosby
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Corresponding author

Correspondence to Mariam Doliashvili .

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Editors and Affiliations

  1. Soar Technology Inc., Orlando, FL, USA

    Dylan D. Schmorrow

  2. Katmai Government Services, Orlando, FL, USA

    Cali M. Fidopiastis

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Doliashvili, M., Ogawa, MB.C., Crosby, M.E. (2022). Using Augmented Cognition to Examine Differences in Online Handwriting Recognition for Native and Non-native Writers. In: Schmorrow, D.D., Fidopiastis, C.M. (eds) Augmented Cognition. HCII 2022. Lecture Notes in Computer Science(), vol 13310. Springer, Cham. https://doi.org/10.1007/978-3-031-05457-0_5

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  • DOI: https://doi.org/10.1007/978-3-031-05457-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05456-3

  • Online ISBN: 978-3-031-05457-0

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