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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM34747

Language Enabled Image Originator

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM34747

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM34747

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

AJ

Akarsh J

DDVC

Dr. Devaraj Verma C

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Abstract

Traditionally, forensic artists would painstakingly sketch a suspect’s face from a witness’s \statement in order to create forensic photographs. There are restrictions on this procedure, though. First, it depends a great deal on the interpretation of the artist, which can bring falsehoods and prejudices. It can also take a lot of time, particularly if the drawing needs to be refined repeatedly. Image generation is the process of creating new pictures that are comparable to the people in a certain dataset. Producing visually realistic pictures that fit the input’s properties is the aim of image creation. information. Machine learning employs a number of pictures generating approaches, such as auto-regressive models, variational autoencoders (VAEs), generative adversarial networks (GANs), and stable diffusion models. These models are trained on an image dataset (for instance, 5,85B CLIP-filtered image-text pairings make up the large-scale research dataset LAION 5B) and are taught to produce new pictures that are comparable to the original data. An image generating model may be used to create a series of pictures for forensic sketching, with the witness’s description serving as the basis for selecting the best image. This and the necessary face feature changes may be fed into the Image-to-Image Translation model. Until an adequate picture is produced, the picture-to-Image Translation model creates a fresh series of images with alterations.

KEYWORDS

variational autoencoders (VAEs) Generative adversarial networks (GANs)

Cite This Article

Akarsh J, Dr. Devaraj Verma C, "Language Enabled Image Originator," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM34747

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Publication Date

12/31/45441

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Double-Blind Peer Review

Review Rounds: 2

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