A Hybrid Classifier for Handwriting Recognition on Multi-domain Financial Bills Based on DCNN and SVM
Bibliographic record
Abstract
With the rapid growth of the global economy, the automatic recognition of financial bills becomes the primary way to reduce the burden of the traditional manual approach for bill recognition and classification. However, most automatic recognition methods cannot effectively recognize the handwritten characters on financial bills, especially when the bills come from different financial companies. To solve the problem, this paper fully explores the bill system in banks and the operations of bill number recognition, and then develops a hybrid classifier based on deep convolutional neural network (DCNN) and support vector machine (SVM), with the aim to recognize the handwritten numbers on financial bills in different domains. The DCNN with different channels was adopted to effectively mine the local handwritten numbers on financial bills from varied sources. Then, the extracted information was fed to the SVM to realize accurate classification of numbers. Our method makes full use of the distribution difference between information in different fields, and adapts to different fields based on the parameter sharing mechanism. Experimental results show that our method can recognize the handwritten numbers on financial bills more accurately (>3%) than benchmark methods.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".