Optimizing image format piping and instrumentation diagram recognition: Integrating symbol and text recognition with a single backbone architecture
Bibliographic record
Abstract
Abstract Recent studies propose deep learning-based methods to recognize symbols and text in Piping and Instrumentation Diagrams (P&ID). However, existing approaches use complex processes with separate models for symbol detection, text detection, and text recognition. We propose an integrated model combining symbol-text detection and text recognition modules using a text spotting method. Our model extracts text region features encoded with local character information, enabling a lightweight text recognition module that reduces processing time. The integrated approach allows end-to-end learning between modules, facilitating semantic information transmission and improving overall performance compared to multi-model architecture. When tested on industrial P&ID images, our model achieved high performance with an IoU threshold of 0.5: maximum precision of 0.9763/0.9527, recall of 0.9521/0.9075, and F1 score of 0.9640/0.9295 for symbol-text detection/text recognition.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.002 |
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".