Evaluating Azure Document Intelligence for complex PDFs - Table Extraction

Azure Document Intelligence was evaluated as part of the RD-TableBench study, assessing its capability to extract data from complex table images against other market solutions. With a precision score of 82.7%, Azure performed well in basic table extraction but lagged behind Reducto's 90.2% accuracy, highlighting a notable gap that becomes critical in mission-critical document processing tasks. While Azure surpasses AWS Textract Tables and vastly outperforms Google Cloud Document AI, it struggles with complex hierarchical structures, dense text, and unconventional layouts, similar to other conventional cloud-based document parsing tools. Although it exceeds the performance of vision language models like GPT-4o, Azure's limitations in recognizing sophisticated table hierarchies and handling dense content suggest that organizations requiring the highest accuracy should consider more advanced solutions like Reducto, especially in scenarios involving large-scale document processing where even minor accuracy improvements can yield significant operational benefits.

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