Fine-tuning DeepSeek-OCR-2 for Molecular Structure Recognition
Optical Chemical Structure Recognition (OCSR) is critical for converting 2D molecular diagrams from printed literature into machine-readable formats. While Vision-Language Models have shown promise in end-to-end OCR tasks, their direct application to OCSR remains challenging, and direct full-parameter supervised fine-tuning often fails. In this work, we adapt DeepSeek-OCR-2 for molecular optical recognition by formulating the task as image-conditioned SMILES generation. To overcome training instabilities, we propose a two-stage progressive supervised fine-tuning strategy: starting with parameter-efficient LoRA and transitioning to selective full-parameter fine-tuning with split learning r...
| Authors | Haocheng Tang, Xingyu Dang, Junmei Wang |
|---|---|
| Primary category | cs.CV |
| Published | 3 April 2026 |
| Last updated at source | 21 April 2026 |
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