Optimizing PDF Data for LLM Applications: A Comparative Analysis of Tools and Techniques for Handling Complex Tables and Mixed Content

Optimizing PDF Data for LLMApplications: A Comparative Analysisof Tools and Techniques for HandlingComplex Tables and Mixed Content Authors Madhusri Shanmugam Dr. Varsha Jain Madhusri Shanmugam has over 12 years of experiencein the IT field and has been specializing as a Pythondeveloper. Over the years, she has honed her skills invarious domains, particularly in automation, where shehas contributed to the development, implementation,and optimization of efficient automation workflows.Currently, she is focusing on GenAI development,exploring the intersection of artificial intelligence andautomation to build cutting-edge solutions. She ispassionate about continuous learning and constantlyrefining her knowledge and skills to keep herself up todate with industry trends. Dr. Varsha Jain comes with over 26 years of rich ITexperience working with both mid-sized and largeservice-based organizations. She holds a Doctoratespecializing in AI from the Swiss School of B&M.She has led a diverse portfolio of data scienceprojects involving Generative AI, computer vision, andsupervised and unsupervised learning, among others.She has also authored numerous papers, participatedin panel discussions, and hosted knowledge sessionstailored to a wide range of audiences. She has a strongresearch mindset, and her latest research area is in thefield of Quantum Machine Learning. Anjali Kumari Anjali Kumari is a GenAI software developer with a BTech degree in Computer Science from Haldia Institute ofTechnology. With over 14 months of experience, she specializes in building innovative solutions using Azure platformsand OpenAI LLMs. Her expertise in NLP and GenAI allows her to develop applications that optimize and automatecomplex tasks. Anjali is adept at utilizing advanced AI tools for data extraction, analysis, and automation, and excelsin integrating diverse technologies to enhance data processing capabilities. Her commitment to addressing real-worldchallenges is evident in her ability to evaluate AI models for performance and efficiency, and she is passionate aboutcreating impactful solutions through GenAI. As the adoption of Large Language Models (LLMs) continues to expandacross industries, the ability to accurately extract and analyze data fromcomplex PDF documents has become increasingly critical. LLMs excel inprocessing and interpreting text, yet they encounter significant challengeswhen dealing with content extracted from tables and images, particularlywhen the structural integrity of these elements is compromised. In an erawhere businesses rely heavily on data-driven insights, The failure to maintainthis integrity can lead to incomplete or inaccurate analyses, ultimatelyimpacting decision-making processes. This paper addresses these challenges by evaluating various tools andtechniques designed to extract tables, images, and text from PDFs, explicitlyfocusing on maintaining the integrity of complex tables. These include tableswith merged rows and columns, color-coded cells, and tables spanningmultiple pages—scenarios common in real-world documents but notoriouslydifficult for extraction tools to handle. Furthermore, the paper explores theimplications of these extraction processes for LLMs and multimodal models,which require high-quality input to generate reliable insights. This paper provides critical insights into their strengths and limitations bycomprehensively analyzing various tools, including AWS Textract, AzureDocument Intelligence, Adobe Extract PDF, Unstructured I/O, Tabula, andSpire PDF. It also examines the necessity of combining multiple tools toachieve optimal results, particularly in complex use cases. Moreover, thepaper highlights innovative approaches for enhancing LLM performance,such as using summaries or HTML formats for tables within chunked data inRetrieval-Augmented Generation (RAG)-based applications. Ultimately, this paper aims to empower industry professionals with theknowledge and strategies needed to extract and utilize data from PDFseffectively, ensuring that LLMs and multimodal models can deliver accurate,actionable insights in even the most challenging scenarios. Contents 01Introduction02Problem Statement03Literature Review 04Data and Methodology •Document Selection and Evaluation Approach•Case Study Methodology•Data Selection and Evaluation Methodology Summary 05Evaluation of Tools and Techniques •AWS Textract•Tabula•Spire•Azure Document Intelligence•Unstructured I/O•Adobe Pdf Services 06Comparative Analysis of Tools 07Case Study: Financial Report - LLM Performance Analysis •Case Study Overview•Tool Performance: Approaches, Challenges, and Optimization•Processing Efficiency and Cost-Effectiveness•Final Results•Case Study Takeaway 08Conclusion •Combining Tools for Enhanced Accuracy•RAG-Based Applications and Chunking PDFs•Final Thoughts 09References In the rapidly evolving data analysis landscape, leveraging Large LanguageModels (LLMs) has become a powerful tool for deriving