Taele, Paul Piula (2010-12). Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages. Master's Thesis. Thesis uri icon


  • One of the challenges students face in studying an East Asian (EA) language

    (e.g., Chinese, Japanese, and Korean) as a second language is mastering their selected

    language's written component. This is especially true for students with native fluency of

    English and deficient written fluency of another EA language. In order to alleviate the

    steep learning curve inherent in the properties of EA languages' complicated writing

    scripts, language instructors conventionally introduce various written techniques such as

    stroke order and direction to allow students to study writing scripts in a systematic

    fashion. Yet, despite the advantages gained from written technique instruction, the

    physical presence of the language instructor in conventional instruction is still highly

    desirable during the learning process; not only does it allow instructors to offer valuable

    real-time critique and feedback interaction on students' writings, but it also allows

    instructors to correct students' bad writing habits that would impede mastery of the

    written language if not caught early in the learning process.

    The current generation of computer-assisted language learning (CALL)

    applications specific to written EA languages have therefore strived to incorporate

    writing-capable modalities in order to allow students to emulate their studies outside the classroom setting. Several factors such as constrained writing styles, and weak feedback

    and assessment capabilities limit these existing applications and their employed

    techniques from closely mimicking the benefits that language instructors continue to

    offer. In this thesis, I describe my geometric-based sketch recognition approach to

    several writing scripts in the EA languages while addressing the issues that plague

    existing CALL applications and the handwriting recognition techniques that they utilize.

    The approach takes advantage of A Language to Describe, Display, and Editing in

    Sketch Recognition (LADDER) framework to provide users with valuable feedback and

    assessment that not only recognizes the visual correctness of students' written EA

    Language writings, but also critiques the technical correctness of their stroke order and

    direction. Furthermore, my approach provides recognition independent of writing style

    that allows students to learn with natural writing through size- and amount-independence,

    thus bridging the gap between beginner applications that only recognize single-square

    input and expert tools that lack written technique critique.

publication date

  • December 2010