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PPTLens: Create Digital Objects with Sketch Images

Published: 13 October 2015 Publication History

Abstract

In this work, we introduce the PPTLens system to convert sketch images captured by smart phones to digital flowcharts in PowerPoint. Different from existing sketch recognition system, which is based on hand-drawn strokes, PPTLens enables users to use sketch images as inputs directly. It's more challenging since strokes extracted from sketch images might not only be very messy, but also without temporal information of the drawings. To implement the 'Image to Object' (I2O) scenario, we propose a novel sketch image recognition framework, including an effective stroke extraction strategy and a novel offline sketch parsing algorithm. By enabling sketch images as inputs, our system makes flowchart/diagram production much more convenient and easier.

References

[1]
C. Carton, A. Lemaitre, and B. Couasnon. Fusion of statistical and structural information for flowchart recognition. In ICDAR, 2013.
[2]
B. Epshtein, E. Ofek, and Y. Wexler. Detecting text in natural scenes with stroke width transform. CVPR, 2010.
[3]
L. B. Kara and T. F. Stahovich. Hierarchical parsing and recognition of hand-sketched diagrams. UIST, 2004.
[4]
E. J. Peterson, T. F. Stahovich, E. Doi, and C. Alvarado. Grouping Strokes into Shapes in Hand-Drawn Diagrams. Artificial Intelligence, 2010.
[5]
J. Wu, C. Wang, L. Zhang, and Y. Rui. Sketch Recognition with Natural Correction and Editing. AAAI, 2014.
[6]
Z. Zhang and L. W. He. Whiteboard scanning and image enhancement. Digital Signal Processing, 2007.

Cited By

View all
  • (2020)Lightweight Neural Network for Sketch Recognition on Mobile PhonesScience and Technologies for Smart Cities10.1007/978-3-030-51005-3_35(428-439)Online publication date: 28-Jul-2020
  • (2019)Sketch recognition using transfer learningMultimedia Tools and Applications10.1007/s11042-018-7067-178:12(17095-17112)Online publication date: 1-Jun-2019

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Information

Published In

cover image ACM Conferences
MM '15: Proceedings of the 23rd ACM international conference on Multimedia
October 2015
1402 pages
ISBN:9781450334594
DOI:10.1145/2733373
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

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Author Tags

  1. offline sketch recognition
  2. sketched flowchart recognition

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  • Demonstration

Funding Sources

  • National Natural Science Foundation of China
  • National Key Basic Research Program of China

Conference

MM '15
Sponsor:
MM '15: ACM Multimedia Conference
October 26 - 30, 2015
Brisbane, Australia

Acceptance Rates

MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all
  • (2020)Lightweight Neural Network for Sketch Recognition on Mobile PhonesScience and Technologies for Smart Cities10.1007/978-3-030-51005-3_35(428-439)Online publication date: 28-Jul-2020
  • (2019)Sketch recognition using transfer learningMultimedia Tools and Applications10.1007/s11042-018-7067-178:12(17095-17112)Online publication date: 1-Jun-2019

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