@Article{M-10157,
AUTHOR = {Patel, Dharti and Pandit, Dr. H. B.},
TITLE = {Case Study: Centralizing Diverse E-Commerce Invoices Using Invoice LLM Model},
JOURNAL = {Scientific Research Journal of Science, Engineering and Technology},
VOLUME = {2},
YEAR = {2024},
NUMBER = {2},
ARTICLE-NUMBER = {M-10157},
URL = {https://isrdo.org/journal/SRJSET/currentissue/case-study-centralizing-diverse-e-commerce-invoices-using-invoice-llm-model},
ISSN = {2584-0584},
ABSTRACT = {E-commerce platforms handle various invoices, including PDFs, handwritten documents, and scanned JPG images. This diversity in invoice formats presents significant challenges when centralising data for accounting and tax purposes. Manual processing leads to operational inefficiencies and limits scalability. This case study discusses how integrating an Invoice LLM (Large Language Model), combined with Optical Character Recognition (OCR) for handwritten and scanned invoices, helps extract and centralise key entities from different formats, reducing manual intervention and increasing operational efficiency.},
DOI = {}
}