Djupinlärning för klassifikation av inskannade dokument : Vad är viktigt?
Bibliographic record
Abstract
This thesis investigates how the quality and quantity of data affect the performance of deep learning models in multimodal document classification. It explores when the input documents are represented both as images and as OCR-extracted text from the image. While deep learning models have shown strong performance on clean, large-scale datasets, many applications often involve noisy data and limited annotations, especially in sensitive domains such as healthcare. To study these effects, a series of controlled experiments are designed that simulate degraded data conditions. The training dataset size is reduced and artificial corruptions are applied to both visual image resolution and textual OCR quality modalities. The impact of these manipulations are evaluated both individually and in combination across several degradation levels, using the Ryerson Vision Lab Complex Document Information Processing (RVL-CDIP) dataset as a proxy for medical document collections. The results indicate that image degradation has a slightly greater impact on model performance than reduced text quality or training set size alone. When both modalities are degraded, performance drops significantly, even when using a very large dataset size. These findings suggest that deep multimodal models are particularly sensitive to visual input quality. But as long as at least one modality remains informative, the model can maintain reasonable accuracy even with limited data. Implementation details, evaluation procedures, and a reproducible pipeline are provided to support further research in low-resource settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".