Extracting data from complex nested forms is a common challenge in document processing. This benchmark tests several OCR solutions on forms with intricate layouts, such as multi-level tables and conditional sections. The results show that while most tools achieve high text recognition accuracy, they struggle to preserve the logical structure—like which fields belong to which section or how items are hierarchically related. This gap is critical for downstream tasks like automated data entry or invoice processing, where structure matters as much as text. The article suggests that future OCR systems need to integrate layout understanding and semantic parsing to be truly useful for business workflows. For developers building document AI pipelines, this benchmark provides actionable insights into tool selection and highlights areas where custom post-processing is still necessary.
A comparative evaluation of OCR tools on complex nested forms reveals that most recognize text but fail to reconstruct the underlying business structure, highlighting a key gap in document AI.