Recognition-free Question Answering on Handwritten Document Collections

TL;DR

This paper introduces a recognition-free question answering method tailored for handwritten document collections, improving retrieval and answering accuracy on challenging datasets without relying on handwriting recognition.

Contribution

It presents a novel recognition-free QA approach with robust retrieval and two models, outperforming existing recognition-free methods on benchmark datasets.

Findings

Outperforms state-of-the-art recognition-free QA models on BenthamQA and HW-SQuAD datasets

Introduces a robust document retrieval method for handwritten collections

Develops two QA models tailored for handwritten document images

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.