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 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.

翻译：现有分子图解的视觉解析器将基于像素的栅格图像（如PNG）转换为化学结构表示（例如SMILES）。然而，由包括LaTeX和Word在内的文字处理器生成的PDF提供了字符、线条和多边形的精确位置与形状。我们从原生数字PDF分子图像中提取符号，然后应用简单的图变换以可编辑的ChemDraw文件（CDXML）形式捕获视觉与化学结构。我们的快速流水线（PDF → 视觉图 → 化学图）无需GPU、光学字符识别（OCR）或矢量化。我们使用SMILES字符串在标准基准上进行评估，同时采用一种基于图度量及LgEval错误汇编的新型评估方法。原生数字PDF中的几何信息产生高精度的解析器，激励利用提取的图形、视觉结构与化学结构作为标注，为从栅格图像识别的视觉解析器生成训练数据。为此，我们在Indigo中渲染SMILES字符串，解析分子结构，然后验证识别出的结构以筛选正确的文件。