AN AI-DRIVEN CLOUD-BASED RECRUITMENT VERIFICATION FRAMEWORK USING COMPUTER VISION AND NATURAL LANGUAGE PROCESSING

Recruiters often struggle to check candidates’ credentials, identities, and resumes quickly when they have to handle many applications. This work tackles that problem with an AI‑driven, cloud‑based verification system that combines computer vision and natural‑language processing. The idea is still conceptual because the original paper gives few details, but the proposed design works like this: * Computer‑vision modules examine uploaded documents. They verify a person’s identity, match faces to photos, and check whether the documents look authentic. * At the same time, NLP models read the text in resumes, transcripts, and online profiles. They pull out key information and compare it across sources to spot any mismatches. All of these

models run on cloud servers, so the system can scale to handle many requests and give HR teams near‑real‑time results. For M.Tech students, the framework provides a solid base for research on multimodal data fusion—mixing visual features from documents with the meaning extracted from text to judge a candidate’s authenticity. Future work could measure how fast the cloud inference runs and look into ways to reduce bias in the automated screening.

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