Optimizing image format piping and instrumentation diagram recognition: Integrating symbol and text recognition with a single backbone architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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