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Paper ID:IJSREM64642

DESIGNER-CENTRIC IDE: ACCURATE INTERPRETATION OF HAND DRAWN WEB LAYOUTS INTO FULLY FUNCTIONAL CODE

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64642

Volume:

10 |

6 |

Jun 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64642

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KJA

KANNAMMAL J A

Department of Master of Computer Applications

Vivekanandha Institute of Information and Management Studies, Tamil Nadu, India

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DSS

Dr. S. SENTHAMARAISELVI

Department of Master of Computer Applications

Vivekanandha Institute of Information and Management Studies, Tamil Nadu, India

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DSA

Dr. S. ANITHA

Department of Master of Computer Applications

Vivekanandha Institute of Information and Management Studies, Tamil Nadu, India

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Abstract

Current Integrated Development Environments (IDEs) primarily focus on manual coding and offer limited support for converting visual designs into executable code. This creates a significant gap between design creation and implementation. To overcome this limitation, this project proposes a designer-centric IDE capable of transforming hand-drawn web layouts into structured HTML and CSS code automatically. The system utilizes deep learning techniques to identify user interface elements from sketches and preserve their spatial arrangement. A combination of feature extraction and sequence-based modeling ensures that the generated code reflects the original design accurately. Additionally, the system provides real-time execution and visual feedback, enabling faster prototyping and reducing development effort. This approach improves productivity and bridges the gap between designers and developers in modern web development workflows.

KEYWORDS

Designer-Centric IDE Sketch Recognition HTML Code Generation Deep Learning Web Development UI Detection.

Cite This Article

KANNAMMAL J A, Dr. S. SENTHAMARAISELVI, Dr. S. ANITHA, "DESIGNER-CENTRIC IDE: ACCURATE INTERPRETATION OF HAND DRAWN WEB LAYOUTS INTO FULLY FUNCTIONAL CODE," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-6, Jun 2026. https://doi.org/10.55041/IJSREM64642

Full Article/Conclusion

The proposed Designer-Centric IDE helps convert hand-drawn web sketches into HTML and CSS code automatically. The system combines sketch recognition, code generation, and webpage preview in a single platform. Using deep learning techniques such as FPN, ResNet-50, and Seq2Seq with Attention, the system can identify UI components and generate webpage code while maintaining the layout structure. The results show that the proposed approach supports accurate and efficient webpage development. The system reduces development time and makes the design-to-code process easier for users. In the future, the project can be improved by adding responsive webpage generation and advanced UI recognition features.

References

[1]  A. Deolekar, K. Dhanawade, R. Chavan and S. Bhambure, "UI Code Generation using Deep Learning", International Research Journal of Engineering and Technology, vol. 8, no. 05, 2021.   [2]  D. Soselia, K. Saifullah, and T. Zhou, "Learning UI-To-Code Reverse Generator Using Visual Critic Without Rendering," arXiv preprint, 2023.   [3]   G. Vitkare, R. Jejurkar, S. Kamble, Y. Thakare and A. P. Lahare, "Automated HTML Code Generation From Hand Drawn Images Using Machine Learning Methods", vol. 04, no. 01, Jan. 2022.   [4]   D. S. Baulé, C. G. von Wangenheim, A. von Wangenheim, J. C. R. Hauck, and E. C. Vargas Júnior, "Automatic Code Generation from Sketches of Mobile Applications in End-User Development Using Deep Learning," arXiv preprint arXiv:2103.05704, 2021.   [5]    S. Patil, R. Pawar, S. Punder and J. John, "Generation of HTML Code Using Machine Learning Techniques from Mock-Up Images", International Research Journal of Engineering and Technology, vol. 7, no. 03, 2020.   [6]   D. Yashaswini, Sneha, and N. Kumar, "HTML Code Generation from Website Images and Sketches using Deep Learning-Based Encoder-Decoder Model," in 2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), 2022.   [7]    V. Kalbande, K. Meshram, S. Somnathe, R. Deshmukh and M. Mohod, "Automatic HTML Code Generation from Mock-Up Images Using Machine Learning Techniques", International Research Journal of Engineering and Technology, vol. 8, no. 06, 2021.   [8]   Yong Xu, Lili Bo, Xiaobing Sun, Bin Li, Jing Jiang and Wei Zhou, "Image2Emmet: Automatic Code Generation from Web User Interface Image", Journal of Software Evolution and Process, vol. 33, e2369, 2021. [9]   B. Cai, J. Luo, and Z. Feng, "A Novel Code Generator for Graphical User Interfaces," Scientific Reports, vol. 13, Article 20329, 2023.

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Publication Date

6/8/2026

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Double-Blind Peer Review

Review Rounds: 2

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