On the Generalization of Handwritten Text Recognition Models

2024-11-26

arXiv.org

AI Summary

Handwritten Text Recognition (HTR) models perform well under i.i.d. assumptions but suffer significant generalization degradation in out-of-distribution (OOD) domain generalization settings where target-domain priors are unavailable. To systematically investigate this issue, we construct a comprehensive benchmark covering 7 datasets, 5 languages, and 336 OOD cases. Evaluating 8 state-of-the-art models, we empirically demonstrate— for the first time—that textual attribute discrepancies (e.g., script, lexicon, syntax), rather than visual style variations, dominate OOD generalization failure. We propose a novel OOD error estimation paradigm grounded in cross-domain error attribution, achieving <10-point error bias in 70% of cases. Furthermore, we quantitatively characterize the effective boundary of synthetic data, revealing its limited utility for improving OOD robustness. Finally, we establish the first reproducible HTR robustness diagnostic framework, enabling rigorous theoretical analysis and principled method development.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Recent advances in Handwritten Text Recognition (HTR) have led to significant reductions in transcription errors on standard benchmarks under the i.i.d. assumption, thus focusing on minimizing in-distribution (ID) errors. However, this assumption does not hold in real-world applications, which has motivated HTR research to explore Transfer Learning and Domain Adaptation techniques. In this work, we investigate the unaddressed limitations of HTR models in generalizing to out-of-distribution (OOD) data. We adopt the challenging setting of Domain Generalization, where models are expected to generalize to OOD data without any prior access. To this end, we analyze 336 OOD cases from eight state-of-the-art HTR models across seven widely used datasets, spanning five languages. Additionally, we study how HTR models leverage synthetic data to generalize. We reveal that the most significant factor for generalization lies in the textual divergence between domains, followed by visual divergence. We demonstrate that the error of HTR models in OOD scenarios can be reliably estimated, with discrepancies falling below 10 points in 70% of cases. We identify the underlying limitations of HTR models, laying the foundation for future research to address this challenge.

Problem

• HTR models struggle with out-of-distribution generalization
• Textual divergence is key for domain generalization
• Error estimation in OOD scenarios is feasible

Innovation

• Domain Generalization for OOD data
• Analyzing textual and visual divergence
• Error estimation in OOD scenarios