RFL: Simplifying Chemical Structure Recognition with Ring-Free Language
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917909490565120 |
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| author | Chang, Qikai Chen, Mingjun Pi, Changpeng Hu, Pengfei Zhang, Zhenrong Ma, Jiefeng Du, Jun Yin, Baocai Hu, Jinshui |
| author_facet | Chang, Qikai Chen, Mingjun Pi, Changpeng Hu, Pengfei Zhang, Zhenrong Ma, Jiefeng Du, Jun Yin, Baocai Hu, Jinshui |
| contents | The primary objective of Optical Chemical Structure Recognition is to identify chemical structure images into corresponding markup sequences. However, the complex two-dimensional structures of molecules, particularly those with rings and multiple branches, present significant challenges for current end-to-end methods to learn one-dimensional markup directly. To overcome this limitation, we propose a novel Ring-Free Language (RFL), which utilizes a divide-and-conquer strategy to describe chemical structures in a hierarchical form. RFL allows complex molecular structures to be decomposed into multiple parts, ensuring both uniqueness and conciseness while enhancing readability. This approach significantly reduces the learning difficulty for recognition models. Leveraging RFL, we propose a universal Molecular Skeleton Decoder (MSD), which comprises a skeleton generation module that progressively predicts the molecular skeleton and individual rings, along with a branch classification module for predicting branch information. Experimental results demonstrate that the proposed RFL and MSD can be applied to various mainstream methods, achieving superior performance compared to state-of-the-art approaches in both printed and handwritten scenarios. The code is available at https://github.com/JingMog/RFL-MSD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07594 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RFL: Simplifying Chemical Structure Recognition with Ring-Free Language Chang, Qikai Chen, Mingjun Pi, Changpeng Hu, Pengfei Zhang, Zhenrong Ma, Jiefeng Du, Jun Yin, Baocai Hu, Jinshui Computer Vision and Pattern Recognition The primary objective of Optical Chemical Structure Recognition is to identify chemical structure images into corresponding markup sequences. However, the complex two-dimensional structures of molecules, particularly those with rings and multiple branches, present significant challenges for current end-to-end methods to learn one-dimensional markup directly. To overcome this limitation, we propose a novel Ring-Free Language (RFL), which utilizes a divide-and-conquer strategy to describe chemical structures in a hierarchical form. RFL allows complex molecular structures to be decomposed into multiple parts, ensuring both uniqueness and conciseness while enhancing readability. This approach significantly reduces the learning difficulty for recognition models. Leveraging RFL, we propose a universal Molecular Skeleton Decoder (MSD), which comprises a skeleton generation module that progressively predicts the molecular skeleton and individual rings, along with a branch classification module for predicting branch information. Experimental results demonstrate that the proposed RFL and MSD can be applied to various mainstream methods, achieving superior performance compared to state-of-the-art approaches in both printed and handwritten scenarios. The code is available at https://github.com/JingMog/RFL-MSD. |
| title | RFL: Simplifying Chemical Structure Recognition with Ring-Free Language |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.07594 |