Analysis & Modeling of Deep Learning Techniques for Flowchart to Code Generation

This research basically explores the analysis and modelling of deep learning techniques. Advanced AI is being integrated and to automate the flowchart-to-code generation process, vision models is used. A popular pipeline is being introduce which involves more than one service and APIs. Also, the methodology of central/main API includes the interaction between a variety of components such as uploading of an image, processing services, and code generation modules by AI. GPT-4 Vision API, which is a deep learning vision model, which extracts visual and contextual information from the uploaded flowchart, then to generate the appropriate code, it is being interpreted by a language model. Now, for user validation and deployment, this generated code if being returned to the frontend interface. How the translation of visual actions into code can be automated by deep learning models is being represented in this paper. Also, it boosts the productivity and lessens the need for the involvement of the human. A thorough review of the system's design, performance assessment, and deep learning techniques is been included in this paper. The conclusion shows how the suggested approach is successful and can be represented as a new resource for those who are looking for a tool that will generate the visual representation through coding. The processed code, which is displayed to the user will

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Publication Details

Published
2025-05-29
DOI
https://doi.org/10.1109/netcrypt65877.2025.11102763
Citations
1
Primary Topic
Speech Recognition and Synthesis
Type
article
Field-Weighted Citation Impact
2.48
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Analysis & Modeling of Deep Learning Techniques for Flowchart to Code Generation

Saurabh Sharma, Vinay Chaudhary, Shiraz Khurana, Gaurav Raj et al.
1 citations
Speech Recognition and Synthesis
2.48
article

Analysis & Modeling of Deep Learning Techniques for Flowchart to Code Generation

Saurabh Sharma, Vinay Chaudhary, Shiraz Khurana, Gaurav Raj, Amer M. Johri
article en
1 citations

Abstract

This research basically explores the analysis and modelling of deep learning techniques. Advanced AI is being integrated and to automate the flowchart-to-code generation process, vision models is used. A popular pipeline is being introduce which involves more than one service and APIs. Also, the methodology of central/main API includes the interaction between a variety of components such as uploading of an image, processing services, and code generation modules by AI. GPT-4 Vision API, which is a deep learning vision model, which extracts visual and contextual information from the uploaded flowchart, then to generate the appropriate code, it is being interpreted by a language model. Now, for user validation and deployment, this generated code if being returned to the frontend interface. How the translation of visual actions into code can be automated by deep learning models is being represented in this paper. Also, it boosts the productivity and lessens the need for the involvement of the human. A thorough review of the system's design, performance assessment, and deep learning techniques is been included in this paper. The conclusion shows how the suggested approach is successful and can be represented as a new resource for those who are looking for a tool that will generate the visual representation through coding. The processed code, which is displayed to the user will

Gautam Buddha University (IN), Sharda University (IN)
Openalex Percentile: Top 9%
Speech Recognition and Synthesis
2.48
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Analysis & Modeling of Deep Learning Techniques for Flowchart to Code Generation — Saurabh Sharma, Vinay Chaudhary, et al. · (2025) | TGRS Research Map | TGRS