Beyond Document Page Classification: Design, Datasets, and Challenges

TL;DR

This paper emphasizes the importance of realistic multi-page document classification benchmarks, introduces new datasets, and discusses challenges and future directions for practical applications.

Contribution

It formalizes various multi-page document classification tasks, highlights dataset gaps, and advocates for more comprehensive evaluation methodologies.

Findings

Current benchmarks are outdated and insufficient for real-world documents.

Proposed datasets better reflect practical multi-page document scenarios.

Evaluation should include calibration, complexity, and distribution shift assessments.

Abstract

This paper highlights the need to bring document classification benchmarking closer to real-world applications, both in the nature of data tested ($X$: multi-channel, multi-paged, multi-industry; $Y$: class distributions and label set variety) and in classification tasks considered ($f$: multi-page document, page stream, and document bundle classification, ...). We identify the lack of public multi-page document classification datasets, formalize different classification tasks arising in application scenarios, and motivate the value of targeting efficient multi-page document representations. An experimental study on proposed multi-page document classification datasets demonstrates that current benchmarks have become irrelevant and need to be updated to evaluate complete documents, as they naturally occur in practice. This reality check also calls for more mature evaluation methodologies, covering calibration evaluation, inference complexity (time-memory), and a range of realistic distribution shifts (e.g., born-digital vs. scanning noise, shifting page order). Our study ends on a hopeful note by recommending concrete avenues for future improvements.}