# Recognition-free Question Answering on Handwritten Document Collections

Source: [Recognition-free Question Answering on Handwritten Document Collections](<https://www.sota2.com/research/paper/68fa6bc11e209131cea3019c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:51.800Z

Authors: Oliver T\\"uselmann, Friedrich M\\"uller, Fabian Wolf, Gernot A. Fink

Published: 2022-02-12T14:47:44.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2202.06080>)

[PDF](<https://arxiv.org/pdf/2202.06080.pdf>)