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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 109
Article Submitted : 16085
Article Published : 7083
Total Authors : 19053
Total Reviewer : 687
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Published Paper Details
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| Paper Title: | Sketch2Code: HTML code generation from sketch |
| Authors Name: | Faizan Rashid Hakeem , Darji Devam Prakash , Dalesh N , Kushagradheeer S , Dr Geetha S |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_182864
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| Published Paper Id: | IJRTI2207154 |
| Published In: | Volume 7 Issue 7, July-2022 |
| DOI: | |
| Abstract: | In the initial phases of program development, user interface (UI) prototyping is a crucial step. A Graphical User Interface (UI) designer's uninspired yet time-consuming duty is to turn designs of a UI into a programmed UI application. This process will be significantly sped up by an automated system that can take the place of humans in the simple execution of UI ideas. The works that support such a system strongly emphasize using UI wireframes as input rather than manually produced sketches. Using a Deep Neural Network that has been trained on a database of such sketches, we offer a novel technique in this research for UI element recognition in input sketches. The specific visual identification task of object detection in sketches is addressed specifically by our deep neural network model. The output of the network is a platform-independent UI representation object. A dictionary of key-value pairs is the object used to represent user interface elements and the properties that go along with them. Our UI parser uses this as input and generates code for many platforms. Because of its inherent platform neutrality, the model can train once and produce UI prototypes for other platforms. This two-step strategy outperforms existing approaches by producing accurate results quickly (on average, 129 ms), without the requirement for two trained models. |
| Keywords: | Sketches, Yolo V5, User-Interface, PyTorch, HTML, Dataset, Machine Learning |
| Cite Article: | "Sketch2Code: HTML code generation from sketch", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 7, page no.996 - 999, July-2022, Available :http://www.ijrti.org/papers/IJRTI2207154.pdf |
| Downloads: | 000204873 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2207154
Registration ID:182864
Published In: Volume 7 Issue 7, July-2022
DOI (Digital Object Identifier):
Page No: 996 - 999 Country: Bangalore, karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2207154 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2207154 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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