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Analysis of Convolutional Neural Networks for Document Image Classification

arXiv 1708.03273 ↗First posted 10 Aug 2017cs.CVcited by 13

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Abstract (arXiv)

Convolutional Neural Networks (CNNs) are state-of-the-art models for document image classification tasks. However, many of these approaches rely on parameters and architectures designed for classifying natural images, which differ from document images. We question whether this is appropriate and conduct a large empirical study to find what aspects of CNNs most affect performance on document images. Among other results, we exceed the state-of-the-art on the RVL-CDIP dataset by using shear transform data augmentation and an architecture designed for a larger input image. Additionally, we analyze the learned features and find evidence that CNNs trained on RVL-CDIP learn region-specific layout features.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Aug 2017.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 4; 2024: 3.

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