Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks
This work addresses the vulnerability of form extraction systems for document processing applications, but it is incremental as it focuses on evaluation rather than proposing a new method.
The paper tackles the problem of evaluating the robustness of transformer-based form field extractors by introducing a framework with 14 novel form transformations, finding that models are highly susceptible to perturbations like field-value variation and text order disarrangement, with F1 score drops of up to 15%.
We propose a novel framework to evaluate the robustness of transformer-based form field extraction methods via form attacks. We introduce 14 novel form transformations to evaluate the vulnerability of the state-of-the-art field extractors against form attacks from both OCR level and form level, including OCR location/order rearrangement, form background manipulation and form field-value augmentation. We conduct robustness evaluation using real invoices and receipts, and perform comprehensive research analysis. Experimental results suggest that the evaluated models are very susceptible to form perturbations such as the variation of field-values (~15% drop in F1 score), the disarrangement of input text order(~15% drop in F1 score) and the disruption of the neighboring words of field-values(~10% drop in F1 score). Guided by the analysis, we make recommendations to improve the design of field extractors and the process of data collection.