A NOVEL AND PRACTICAL SYSTEM FOR VERIFYING SIGNATURES ON PERSIAN HANDWRITTEN BANK CHECKS
Bibliographic record
Abstract
A novel system for verifying signatures on Persian handwritten bank checks is presented, in this paper. The presented system includes two main phases called: training and verification phases. At first, the system is trained using some genuine signatures provided by each customer in training phase. Then verifying the signatures on incoming checks is carried out in the verification phase. Feature extraction step is conducted based on a new approach that uses Multitresolution box-counting (MRBC) method for estimating the fractal dimension of signatures. Here, signature verification is modeled as testing hypothesis, and decision about acceptance or rejection of signatures on incoming checks is carried out using Kolmogorov–Smirnov test. The presented system has been tested on two databases: our new created database and NISDCC database which was used for ICDAR 2009 signature verification competition. Our database has 1000 genuine signatures provided by 100 participants and 200 skilled forgeries copied from genuine samples by five forgers. In total our database includes 1200 Persian signatures. Obtained results show promising performance of the presented system for its application on Persian banks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".