Recognition of courtesy amounts on bank checks based on a segmentation approach
Why this work is in the frame
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Bibliographic record
Abstract
A segmentation based courtesy amount recognition (CAR) system is presented in this paper. A two-stage segmentation module has been proposed, namely the global segmentation stage and the local segmentation stage. At the global segmentation stage, a courtesy amount is coarsely segmented into sub-images according to the spatial relationships of the connected components. These sub-images are then verified by the recognition module and the rejected sub-images are sequentially split using contour analysis at the local segmentation stage. Two neural network classifiers are combined into a recognition module. The isolated digit classifier divides the input patterns into ten numeral classes (0-9), while the holistic double zeros classifier recognizes the cursive and touching double zeros. Experimental results show that the system reads 66.5% bank checks correctly at 0% misreading rate.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it