Automated Information Extraction From Offer Letters Using Deep Learning: A Comparative Study

Ramneet Singh Chadha; Chandrashekhar Aazad; Jasmehar Singh

doi:10.36948/ijfmr.2026.v08i02.72035

Automated Information Extraction From Offer Letters Using Deep Learning: A Comparative Study

Author(s)	Ramneet Singh Chadha, Chandrashekhar Aazad, Jasmehar Singh
Country	India
Abstract	The fast move to digital for HR tasks has led to the creation of a lot of unstructured documents, especially offer letters, which provide important information about employees. Using usual approaches, this has not been processed well for extracting information. This study presents a comprehensive system for the automatic extraction of information from offer letters utilizing optical character recognition (OCR) and named entity recognition. A comparison investigation has been conducted about the performance of the conventional Conditional Random Field (CRF) model and the advanced Bidirectional Encoder Representations from Transformers (BERT) model. This paper presents a system for generating synthetic data to address the challenges of data scarcity and privacy. This framework may create fake data for the process of getting information from offer letters, such as alternative layouts, semantic content, and noise. This paper evaluated the efficacy of the models on a demanding dataset of 500 documents. The findings indicate that, while the traditional Conditional Random Field model yielded satisfactory outcomes, the advanced Bidirectional Encoder Representations from Transformers model produced significantly superior results in comparison to the conventional model. This experimental evaluation also proves the efficiency of the proposed system in extracting key entities such as candidate name, designation, salary, and date of joining accurately from the scanned offer letters. The comparative analysis proves that, although the proposed system using the CRF model works accurately in structured data, the BERT model has a better capacity for understanding the contextual relationship within the document text. Thus, the proposed transformer model proves to be effective in dealing with the variability in the document layouts, linguistic expressions, and document conditions.
Keywords	Document Information Extraction, Named Entity Recognition, OCR, CRF, BERT, Synthetic Data
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-21
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.72035

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 1 Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Automated Information Extraction From Offer Letters Using Deep Learning: A Comparative Study

Share this