Abstract
Existing visual parsers for molecule diagrams translate pixel-based raster images such as PNGs to chemical structure representations (e.g., SMILES). However, PDFs created by word processors including \LaTeX{} and Word provide explicit locations and shapes for characters, lines, and polygons. We %introduce a method to extract symbols from born-digital PDF molecule images and then apply simple graph transformations to capture both visual and chemical structure in editable ChemDraw files (CDXML). Our fast ( PDF $\rightarrow$ visual graph $\rightarrow$ chemical graph ) pipeline does not require GPUs, Optical Character Recognition (OCR) or vectorization. We evaluate on standard benchmarks using SMILES strings, along with a novel evaluation that provides graph-based metrics and error compilation using LgEval. The geometric information in born-digital PDFs produces a highly accurate parser, motivating generating training data for visual parsers that recognize from raster images, with extracted graphics, visual structure, and chemical structure as annotations. To do this we render SMILES strings in Indigo, parse molecule structure, and then validate recognized structure to select correct files.
Abstract (translated)
现有的分子图翻译器将像素基于光栅图像（如PNG）翻译为化学结构表示（例如，SMILES）。然而，由文字处理器生成的PDF提供了字符、线和多边形的明确位置和形状。我们 %引入了一种从出生数字PDF分子图像中提取符号的方法，然后对可编辑的ChemDraw文件（CDXML）应用简单图形变换来捕捉视觉和化学结构。我们的快速（PDF $\rightarrow$ 视觉图形 $\rightarrow$ 化学图形）流程不需要GPU，光学字符识别（OCR）或向量化。我们使用SMILES字符串在标准基准测试上进行评估，同时还使用一种新的评估方法，该方法提供了基于图的指标和利用LgEval进行错误编译。分子数字PDF中的几何信息产生了一个高度准确的解析器，从而 motivation为从光栅图像中识别的视觉解析器生成训练数据，并将提取的图形、视觉结构和化学结构作为注释。为此，我们在Indigo中渲染SMILES字符串，解析分子结构，然后验证识别的结构以选择正确的文件。
URL
https://arxiv.org/abs/2311.12161
