Extraction of Table Information from Annual Reports Supported by CNN and Transformer-Based Approaches

Abstract

Financial tables often feature multi-level headers, grouped categories, and implicit semantics that stretch the limits of current extraction pipelines. Existing literature largely focuses on synthetic or academic datasets, leaving a methodological gap between model development and real-world application. This thesis evaluates TFLOP, a state-of-the-art table extraction model, on a curated set of native PDF annual reports from Swiss companies.