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.

从born-digital PDF分子图像中提取符号并应用简单图形转换，以编辑ChemDraw文件（CDXML），其直接位置和形状信息使得该方法具有高准确性的解析能力，以生成训练数据用于识别栅格图像中的化学结构的视觉解析器。

ChemScraper: PDF图像的图像提取，分子图解析和带注释数据生成