A Transformer-based Math Language Model for Handwritten Math Expression Recognition
This work addresses recognition challenges in handwritten math for educational or document analysis, but it is incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of handwritten mathematical expression recognition by developing a Transformer-based Math Language Model (TMLM) that captures long dependencies among symbols, achieving a perplexity of 4.42 and improving expression rates by up to 2.97 percentage points on benchmark datasets.
Handwritten mathematical expressions (HMEs) contain ambiguities in their interpretations, even for humans sometimes. Several math symbols are very similar in the writing style, such as dot and comma or 0, O, and o, which is a challenge for HME recognition systems to handle without using contextual information. To address this problem, this paper presents a Transformer-based Math Language Model (TMLM). Based on the self-attention mechanism, the high-level representation of an input token in a sequence of tokens is computed by how it is related to the previous tokens. Thus, TMLM can capture long dependencies and correlations among symbols and relations in a mathematical expression (ME). We trained the proposed language model using a corpus of approximately 70,000 LaTeX sequences provided in CROHME 2016. TMLM achieved the perplexity of 4.42, which outperformed the previous math language models, i.e., the N-gram and recurrent neural network-based language models. In addition, we combine TMLM into a stochastic context-free grammar-based HME recognition system using a weighting parameter to re-rank the top-10 best candidates. The expression rates on the testing sets of CROHME 2016 and CROHME 2019 were improved by 2.97 and 0.83 percentage points, respectively.