Evaluation of Deep Convolutional Nets for Document Image Classification\n and Retrieval
Bibliographic record
Abstract
This paper presents a new state-of-the-art for document image classification\nand retrieval, using features learned by deep convolutional neural networks\n(CNNs). In object and scene analysis, deep neural nets are capable of learning\na hierarchical chain of abstraction from pixel inputs to concise and\ndescriptive representations. The current work explores this capacity in the\nrealm of document analysis, and confirms that this representation strategy is\nsuperior to a variety of popular hand-crafted alternatives. Experiments also\nshow that (i) features extracted from CNNs are robust to compression, (ii) CNNs\ntrained on non-document images transfer well to document analysis tasks, and\n(iii) enforcing region-specific feature-learning is unnecessary given\nsufficient training data. This work also makes available a new labelled subset\nof the IIT-CDIP collection, containing 400,000 document images across 16\ncategories, useful for training new CNNs for document analysis.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".