PDF2LaTeX
Zelun Wang, Jyh‐Charn Liu · 2020
The mathematical contents of scientific publications in PDF format cannot be easily analyzed by regular PDF parsers and OCR tools. In this paper, we propose a novel OCR system called PDF2LaTeX, which extracts math expressions and text in both postscript and image-based PDF files and translates them into LaTeX markup. As a preprocessing step, PDF2LaTeX first renders a PDF file into its image format, and then uses projection profile cutting (PPC) to analyze the page layout. The analysis of math expressions and text is based on a series of deep learning algorithms. First, it uses a convolutional neural network (CNN) as a binary classifier to detect math image blocks based on visual features. Next, it uses a conditional random field (CRF) to detect math-text boundaries by incorporating semantics and context information. In the end, the system uses two different models based on a CNN-LSTM neural network architecture to translate image blocks of math expressions and plaintext into the LaTeX representations. For testing, we created a new dataset composed of 102 PDF pages collected from publications on arXiv.org and compared the performance between PDF2LaTeX and the state-of-the-art commercial software InftyReader. The experiment results showed that the proposed system achieved a better recognition accuracy (81.1%) measured by the string edit distance between the predicted LaTeX and the ground truth.