  In recent years, considerable progress has been made in the research area of
Question Answering (QA) on document images. Current QA approaches from the
Document Image Analysis community are mainly focusing on machine-printed
documents and perform rather limited on handwriting. This is mainly due to the
reduced recognition performance on handwritten documents. To tackle this
problem, we propose a recognition-free QA approach, especially designed for
handwritten document image collections. We present a robust document retrieval
method, as well as two QA models. Our approaches outperform the
state-of-the-art recognition-free models on the challenging BenthamQA and
HW-SQuAD datasets.
