Information retrieval from tables in financial documents

Abstract

Structured financial data including balance sheets, income statements, risk metrics need

tables in order to be presented within financial documents. Tables need correct infor- mation extraction for both financial analytic work and monitoring compliance together

for generation of reports. The combination of complex table elements that include cells and merged headers as well as irregular layout structures creates problems for standard processing methods. This paper constructs an information retrieval framework which assesses machine learning, computer vision together with natural language processing,

detection transformer, large language models, Vision language models, convolutional neu- ral network, optical character recognition, structured points of thought to find the most

e!ective strategies for financial document table detection, table structure recognition and

information extraction. The retrieved table relations enable clients to request financial in- formation, historical data and comparisons between di!erent table elements. The analysis

of this research examines various tabular documents to understand interdisciplinary table detection methods through both positive and negative aspects as well as their practical utility in real-world contexts. From various case study reports, the e”ciency of di!erent models, datasets in processing multi-page financial documents, with robust cell detection and data extraction from complicated tabular layouts will define an optimized solution when addressing practical tasks. This research strengthens the connection between table detection methods and semantic retrieval technology to develop inexpensive automated platforms that retrieve financial data and perform analysis.