Bibliographic record
Abstract
Image thresholding and page segmentation are necessary components of any image understanding and recognition system. In order for an OCR to function properly, texts in a document image has to be isolated and then fed to the OCR for recognition. This requires development of a robust and accurate page segmentation technique. In any page segmentation technique, a preprocessing step in terms of image restoration and thresholding is needed. This thesis therefore concentrates on the development of efficient and robust image thresholding and page segmentation algorithms. In this thesis, three efficient contrast enhancement techniques are proposed that in conjunction with the thresholding techniques of Ridler and Calvard constitute the preprocessing step for the image segmentation algorithm. This thesis also provides a survey of the pertinent page segmentation techniques in the literature, and proposes a new block labeling technique based on smearing algorithm. An exhaustive experimentation is conducted in this thesis to demonstrate the efficiency of the proposed techniques.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .L56. Source: Masters Abstracts International, Volume: 42-03, page: 1015. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".