Classification of handwritten annotations in mixed-media documents
Bibliographic record
Abstract
Handwritten annotations in documents contain valuable information, but they are challenging to detect and identify. This paper addresses this challenge. We propose an al-gorithm for generating a novel mixed-media document dataset, Annotated Docset, that consists of 14 classes of machine-printed and handwritten elements and annotations. We also propose a novel loss function, Dense Loss, which can correctly identify small objects in complex documents when used in fully convolutional networks (e.g. U-NET, DeepLabV3+). Our Dense Loss function is a compound function that uses local region homogeneity to promote contiguous and smooth segmentation predictions while also using an L1-norm loss to reconstruct the dense-labelled ground truth. By using regression instead of a probabilistic approach to pixel classification, we avoid the pitfalls of training on datasets with small or underrepre-sented objects. We show that our loss function outperforms other semantic segmentation loss functions for imbalanced datasets, containing few elements that occupy small areas. Experimental results show that the proposed method achieved a mean Intersection-over-Union (mIoU) score of 0.7163 for all document classes and 0.6290 for handwritten annotations, thus outperforming state-of-the-art loss functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".