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This format diversity makes consistent entity extraction difficult for accounting and tax reporting, while manual processing creates inefficiency and limits scalability. The case study presents an Invoice LLM approach combined with OCR to convert non-digital invoice content into machine-readable text, then extract and centralise key fields into a standardised structure. 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This diversity makes it hard to extract consistent entities for financial records at scale.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does the solution handle handwritten and scanned invoices?",{"text":113,"@type":109},"OCR converts handwritten and image-based invoices (e.g., JPGs) into machine-readable text. That digitised text is then processed to extract required invoice entities.",{"name":115,"@type":106,"acceptedAnswer":116},"Which invoice fields are targeted for extraction and standardisation?",{"text":117,"@type":109},"The approach extracts invoice numbers, vendor names, item details, tax information, and totals. Extracted data is mapped into a standardised format for further accounting use.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},335996,1790483910,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":20,"category_name":21,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":79,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":79,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":73},962075114101,"https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165","Case Study: Centralising Diverse E-Commerce Invoices  \nUsing Invoice LLM Model  \nDharti Patel, Research Scholar, Computer Engineering, Sardar Patel University, Vallabh Vidya Nagar  \n(Anand)  \nDr. H. B. Pandit, Professor, Computer Engineering, Sardar Patel University, Vallabh Vidya Nagar (Anand)  \nAbstract  \nE-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.  \nKeywords: E-commerce, Invoice Processing, Large Language Model (LLM), Optical Character Recognition (OCR), Data Extraction, Centralized Data Management, Automation, Scalability  \nBackground  \nThe e-commerce industry handles invoices from thousands of vendors, each with different templates, formats, and delivery methods. These invoices can appear in the following formats:  \n• PDF: Most vendors generate digital invoices in PDF format. These include complex layouts with tables, multiple columns, and embedded images.  \n• Handwritten: Smaller vendors may submit handwritten invoices, which add complexity due to inconsistencies in handwriting styles and legibility.  \n• JPG/Scanned: Some invoices are scanned into JPG format as images of physical invoices or snapshots sent via email or messaging apps.  \nThe lack of a standardised invoice format means the company must process various data inputs. This requires extracting entities from text in PDFs, deciphering handwritten invoices using OCR, and interpreting data from image files like JPGs.  \nProblem Statement  \nThe diversity in invoice formats complicates extracting consistent data for financial records. Manual intervention is not scalable as the platform grows, and handling different formats—especially handwritten and scanned invoices—requires an automated solution that can effectively recognise and centralise data.  \nChallenges  \n1. Handling Multiple Formats: The company receives invoices in various formats, such as PDFs, handwritten paper invoices, and image files. Each format presents unique challenges in terms of extraction and centralisation.  \n2. Manual Processing: Employees manually input data from handwritten and scanned invoices, leading to increased error rates and inefficiencies.  \n3. OCR for Handwritten Invoices: Handwritten invoices pose a unique challenge due to varying handwriting styles, which can affect OCR performance.  \n4. Inconsistent Data Layouts: Even digital PDFs may vary in layout, requiring a solution that can interpret different document structures and extract critical information.  \nSolution: Leveraging Invoice LLM with OCR Integration  \nTo tackle the challenge of diverse invoice formats, the company implemented a two-part solution:  \n1. Optical Character Recognition (OCR): OCR technology converts handwritten and image-based invoices (JPGs) into machine-readable text. This allows for the extraction of key invoice details from non-digital sources.  \n2. Invoice LLM Model: The LLM model processes the OCR-extracted text from handwritten and scanned invoices, as well as text directly from PDF files, to identify and extract critical data such as invoice numbers, vendor names, item details, tax information, and totals. The extracted data is then mapped to a standardised format for further use.  \nImplementation Process  \nFigure: Architecture Diagram  \nThe diagram you shared represents a system for centralising diverse e-commerce invoices using an OCR (Optical Character Recognition) process combined with a GenAI (Generalized Artificial Intelligence) model for ex","cbCailotbUhyD8ze","https://ap.wps.com/l/cbCailotbUhyD8ze","pdf",226679,"English","# Abstract\n# Background\n# Problem Statement\n# Challenges\n# Solution: Leveraging Invoice LLM with OCR Integration\n# Implementation Process","[{\"question\":\"Why does invoice centralisation become difficult in e-commerce operations?\",\"answer\":\"Invoices arrive in many formats with varying templates, layouts, and delivery methods. This diversity makes it hard to extract consistent entities for financial records at scale.\"},{\"question\":\"How does the solution handle handwritten and scanned invoices?\",\"answer\":\"OCR converts handwritten and image-based invoices (e.g., JPGs) into machine-readable text. That digitised text is then processed to extract required invoice entities.\"},{\"question\":\"Which invoice fields are targeted for extraction and standardisation?\",\"answer\":\"The approach extracts invoice numbers, vendor names, item details, tax information, and totals. Extracted data is mapped into a standardised format for further accounting use.\"}]","Case Study - Centralising Diverse E-Commerce Invoices Using Invoice LLM Model | PDF",1790013091]