Baidu Meizu Deep Learning Competition: Arithmetic Operation Recognition Using End-to-End Learning OCR Technologies
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Bibliographic record
Abstract
The end-to-end learning approaches were proposed for an arithmetic expression recognition task in the Baidu Meizu Deep Learning Competition by a deep convolutional neural network (DCNN) with parallel dense layers and component-connection-based detection pipeline with the convolutional recurrent neural network (CRNN) model. Two effective pipelines for DCNN and CRNN to identify long and complex expressions are presented and compared. In the first task, a DCNN connected to parallel dense layers for digital arithmetic operations was developed, which achieves 99.985% accuracy. In the second task, the CRNN with connectionist temporal classification was adopted, combined with the text region detection technique to recognize more complex pictures with both assignment operations and calculation formulas, which achieves 98.087% accuracy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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