15,806 research outputs found
A Sketch-Based Educational System for Learning Chinese Handwriting
Learning Chinese as a Second Language (CSL) is a difficult task for students in English-speaking countries due to the large symbol set and complicated writing techniques. Traditional classroom methods of teaching Chinese handwriting have major drawbacks due to human experts’ bias and the lack of assessment on writing techniques. In this work, we propose a sketch-based educational system to help CSL students learn Chinese handwriting faster and better in a novel way. Our system allows students to draw freehand symbols to answer questions, and uses sketch recognition and AI techniques to recognize, assess, and provide feedback in real time. Results have shown that the system reaches a recognition accuracy of 86% on novice learners’ inputs, higher than 95% detection rate for mistakes in writing techniques, and 80.3% F-measure on the classification between expert and novice handwriting inputs
Transcribing Content from Structural Images with Spotlight Mechanism
Transcribing content from structural images, e.g., writing notes from music
scores, is a challenging task as not only the content objects should be
recognized, but the internal structure should also be preserved. Existing image
recognition methods mainly work on images with simple content (e.g., text lines
with characters), but are not capable to identify ones with more complex
content (e.g., structured symbols), which often follow a fine-grained grammar.
To this end, in this paper, we propose a hierarchical Spotlight Transcribing
Network (STN) framework followed by a two-stage "where-to-what" solution.
Specifically, we first decide "where-to-look" through a novel spotlight
mechanism to focus on different areas of the original image following its
structure. Then, we decide "what-to-write" by developing a GRU based network
with the spotlight areas for transcribing the content accordingly. Moreover, we
propose two implementations on the basis of STN, i.e., STNM and STNR, where the
spotlight movement follows the Markov property and Recurrent modeling,
respectively. We also design a reinforcement method to refine the framework by
self-improving the spotlight mechanism. We conduct extensive experiments on
many structural image datasets, where the results clearly demonstrate the
effectiveness of STN framework.Comment: Accepted by KDD2018 Research Track. In proceedings of the 24th ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining
(KDD'18
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