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This paper addresses an important practical challenge in document analysis systems: achieving reliable localization of structured documents under constraints of limited labeled data and restricted computational resources. The problem is well motivated by real-world industrial applications such as online onboarding and automated document processing, where annotation costs and training efficiency are critical concerns.

The authors propose SDL-Net, a U-Net–inspired encoder–decoder architecture designed to enable data-efficient training and rapid adaptation to new document classes. A key contribution is the separation of encoder pre-training on a diverse, generic document dataset from lightweight decoder fine-tuning for class-specific localization tasks. This design choice allows the model to generalize effectively while reducing the amount of labeled data required for downstream adaptation.

The experimental evaluation on a proprietary dataset demonstrates strong localization performance and supports the claim that the approach generalizes well across document types. The emphasis on practical constraints strengthens the relevance of the work for industrial deployment.

However, the reliance on proprietary data limits reproducibility and independent comparison with existing document localization methods. Additional evaluation on public benchmarks and clearer reporting of computational costs would further strengthen the contribution.

Overall, this paper provides a pragmatic and well-structured approach to data-efficient document localization and offers valuable insights for applied document analysis and information extraction systems.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.