Bigram Label Regularization to Reduce OverSegmentation on Inline Math Expression Detection Inline Mathematical Expression refers to Math Expression (ME) that is blended into plaintext sentences in scientific papers. Detecting inline MEs is a nontrivial problem due to the unrestricted usage of font styles and blurred boundaries with plaintext in scientific publications. For instance, many inline MEs detected by existing algorithms are split into multiple parts incorrectly due to the misidentification of a few characters. In this paper, we propose a bigram regularization model to resolve the split problem in inline ME detection. The model incorporates neighboring constraints during labeling of ME vs. plaintext. Experimental results show that this technique significantly reduces the splits of inline MEs, with small gains in the false and miss rate. In comparison with a CRF model, our model achieves a higher F1 score and a lower miss rate.

Extraction of Math Expressions from PDF Documents Based on Unsupervised Modeling of Fonts This paper proposes a multistage architecture to extract math expressions (ME) from PDF documents based on font analysis. The unsupervised algorithm starts from symbol level analysis based on metadata of PDF objects, including font size, font name, and glyph name. Two subsequent stages utilize a group of spatial and semantic heuristics to merge multiple ME symbols into both inline ME and displayed ME. The algorithm is tested on the Marmot dataset (amended with missing cases). For displayed ME, the proposed method achieved 93.6% precision, 99.4% recall, and 96.4% F1score. For inline ME, the method achieved 92.2% precision, 91.9% recall, and 92.1% F1score. In addition, the algorithm only takes an average of 1.09s to process a page, which is faster than other existing methods.