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This paper presents a new state-of-the-art for document image classificationand retrieval, using features learned by deep convolutional neural networks(CNNs). In object and scene analysis, deep neural nets are capable of learninga hierarchical chain of abstraction from pixel inputs to concise anddescriptive representations. The current work explores this capacity in therealm of document analysis, and confirms that this representation strategy issuperior to a variety of popular hand-crafted alternatives. Experiments alsoshow that (i) features extracted from CNNs are robust to compression, (ii) CNNstrained on non-document images transfer well to document analysis tasks, and(iii) enforcing region-specific feature-learning is unnecessary givensufficient training data. This work also makes available a new labelled subsetof the IIT-CDIP collection, containing 400,000 document images across 16categories, useful for training new CNNs for document analysis.

Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval