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This arXiv preprint proposes using saliency maps to guide data augmentation for handwritten character recognition in low-resource historical collections, and introduces an Explainability Consistency Index to measure how stable those saliency patterns are under perturbation. The reported gains are modest and inconsistent: no single saliency-constrained strategy wins across architectures and corpora, and the authors frame saliency as a tool for controlling augmentation rather than a fixed recipe. For ML readers, the useful takeaway is that similar accuracy can hide different sensitivity to input variation, so explanation-consistency checks may be worth adding to evaluation, though the evidence here is limited to specific datasets and models.
What Not to Erase: Saliency-Constrained Augmentation for Low-Resource Handwritten Character Recognition
arXiv:2610.27309v1 Announce Type: cross Abstract: Optical Character Recognition (OCR) and handwritten character recognition (HCR) in low-resource historical material collections often rely on data augmentation, yet augmentation strategies are typically evaluated almost exclusively through predictive accuracy. This leaves open whether a model's improved performance is accompanied by saliency stability. This question is important in low-resource historical HCR, where recognition is performed on variable and partially degraded letterforms and two models with similar predictive performance may differ in how consistently their saliency patterns respond to such variation. We therefore study saliency-guided augmentation as an explanation-aware strategy for low-resource HCR. Rather than treating saliency maps only as post-hoc interpretations, we use them to define augmentation variants that perturb, preserve, or constrain visually informative regions of character images. We compare classical document-style transformations, random lacunae, and several saliency-constrained lacuna variants. To evaluate the effect of augmentation beyond accuracy, we introduce the Explainability Consistency Index (ECI), a metric that quantifies the stability of saliency patterns under controlled perturbations. We find that saliency-constrained augmentation can improve recognition over classical augmentation, but no single saliency strategy is consistently best. The preferred form of the constraint varies with both architecture and corpus, indicating that saliency is better treated as a mechanism for controlling augmentation than as a fixed augmentation strategy. In some settings, saliency-constrained training improves explanation consistency even when predictive performance is similar. This shows that predictive metrics alone can obscure differences in model sensitivity under perturbation, motivating explanation consistency as a complementary evaluation dimension.
Abstract:Optical Character Recognition (OCR) and handwritten character recognition (HCR) in low-resource historical material collections often rely on data augmentation, yet augmentation strategies are typically evaluated almost exclusively through predictive accuracy. This leaves open whether a model's improved performance is accompanied by saliency stability. This question is important in low-resource historical HCR, where recognition is performed on variable and partially degraded letterforms and two models with similar predictive performance may differ in how consistently their saliency patterns respond to such variation. We therefore study saliency-guided augmentation as an explanation-aware strategy for low-resource HCR. Rather than treating saliency maps only as post-hoc interpretations, we use them to define augmentation variants that perturb, preserve, or constrain visually informative regions of character images. We compare classical document-style transformations, random lacunae, and several saliency-constrained lacuna variants. To evaluate the effect of augmentation beyond accuracy, we introduce the Explainability Consistency Index (ECI), a metric that quantifies the stability of saliency patterns under controlled perturbations. We find that saliency-constrained augmentation can improve recognition over classical augmentation, but no single saliency strategy is consistently best. The preferred form of the constraint varies with both architecture and corpus, indicating that saliency is better treated as a mechanism for controlling augmentation than as a fixed augmentation strategy. In some settings, saliency-constrained training improves explanation consistency even when predictive performance is similar. This shows that predictive metrics alone can obscure differences in model sensitivity under perturbation, motivating explanation consistency as a complementary evaluation dimension.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2610.27309 [cs.CV]
(or arXiv:2610.27309v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.27309 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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