Deep Learning-Based Intelligent Image Recognition and Its Applications in Financial Technology Services
Bibliographic record
Abstract
The financial technology service industry involves a large number of image and text information processing tasks.By automatically processing images and text information, financial institutions can greatly reduce labor costs, improve overall operational efficiency, and help financial institutions identify and predict risks more accurately, thereby improving risk management capabilities.The existing image symbol recognition and scene text detection methods may be affected in terms of recognition accuracy when processing complex scenes, low-resolution images or texts affected by obstacles, distortions and other factors.To this end, this study conducts an in-depth study on the application of deep learning-based intelligent image recognition in financial technology services.It elaborates the application scenarios of image symbol recognition and scene text detection in financial technology services.The ASTER model is improved, and the combination of attention mechanism sequential decoding can effectively capture local information and global dependencies in the feature sequence, thereby improving the recognition accuracy of the image symbol recognition model.By focusing on the center point position information of the text, pixels with the same center point are aggregated to reduce the interference between adjacent texts to some extent, achieving more accurate text segmentation.Experimental results validate the effectiveness of the method in this study.
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 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) | 0.000 | 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) | 0.001 | 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 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".