dptech-corp/molparser/molparser-visual-ocsr

MolParser Visual OCSR Skill

Use this skill when extracting molecular structures from images, PDFs, URLs, or mixed input lists with molparser.MolParser.

Workflow

1. Use molparser/models/config.yaml defaults or pass a custom YAML path to MolParser.
2. Prefer device="auto" unless CPU/GPU must be forced.
3. For PIL images, image paths, or image URLs:

• Use rec_only=True when the image is already a single molecule crop.
• Use rec_only=False when detection is needed; this uses moldet_v2_yolo11n_640_general.pt.

1. For PDF paths or PDF URLs:

• rec_only is ignored; render pages with pypdfium2 at pdf_dpi=300.
• Detect molecules with moldet_v2_yolo11n_960_doc.pt.

1. Recognize crops with the MolParser recognition model, then normalize with postprocess_caption.
2. Use batch defaults unless memory requires tuning: MolDet batch size is 8, MolParser OCSR batch size is 16.
3. Multi-image inputs are batched together; PDF page detections are cropped first, then molecule crops are batched together for recognition.
4. Use moldet.expand_px for detector box expansion and molparser.padding_px for recognition input padding; defaults are expand_px=2 and padding_px=0, and both can be overridden in parse(...).

Minimal Example

from molparser import MolParser
parser = MolParser(device="auto", pdf_dpi=300)
results = parser.parse(["mol.png", "paper.pdf"], rec_only=False)
for item in results:
    print(item.input_index, item.page_index, item.bbox, item.esmi)

For high-throughput molecule crops:

parser = MolParser(device="cuda", molparser_batch_size=1024)
results = parser.parse(["mol_001.png", "mol_002.png"], rec_only=True)

Output Fields

Each result includes source, input_index, page_index, bbox, confidence, raw_caption, caption, smi, esmi, cxsmiles, markush, sru, and groups.

• All parse methods return list[MolParserResult].
• Single image with rec_only=True: one whole-image result; bbox, confidence, and page_index are None.
• Image with rec_only=False: one result per detected molecule; no detections returns an empty list.
• PDF input: one result per detected molecule on rendered PDF pages; page_index, bbox, and confidence are populated.
• List input: flat result list; use input_index to map each result back to the original list item.

Use esmi for downstream tasks that need E-SMILES 2.0 annotations, including axial chirality; smi does not preserve every extension. For token definitions, classification flags, and index handling, read `molparser-extended-smiles/SKILL.md`. An <x> record alone does not set markush. sru is reserved for a symbolic whole-molecule repeat count, not local repeats or numeric counts/ranges.

License Notes

MolDetv2 weights are non-commercial according to their model card. The PyTorch detection path uses Ultralytics YOLO; closed-source, private, SaaS, internal business, or commercial usage may require AGPL-3.0 compliance or an Ultralytics Enterprise license.