Recognition of handwritten mathematical expressions (HMEs) has attracted growing interest
due to steady progress in handwriting recognition techniques and the rapid emergence of …

Given the ubiquity of handwriting and mathematical content in human transactions, machine
recognition of handwritten mathematical text and symbols has become a domain of great …

The CROHME competitions have helped organize the field of handwritten mathematical
expression recognition. This paper presents the evolution of the competition over its first 4 …

Context: Many scientific and technical literature documents contain MSs and MEs that are
more challenging to be recognized by computers than plain text. The recognition of HMSE …

We present the outcome of the latest edition of the CROHME competition, dedicated to on-
line handwritten mathematical expression recognition. In addition to the standard full …

Automatic recognition of mathematical expressions is a challenging pattern recognition
problem since there are many ambiguities at different levels. On the one hand, the …

With the advent and advancement of machine learning and deep learning techniques,
machine-based recognition systems for mathematical text have captivated the attention of …

In this paper, we propose a novel multi-modal attention network (MAN), which is based on
encoder-decoder framework, for handwritten mathematical expression recognition (HMER) …

Long short-term memory networks (LSTM) achieve great success in temporal dependency
modeling for chain-structured data, such as texts and speeches. An extension toward more …

Tools based on machine learning (ML) have seen widespread application in the prediction
and categorization of mathematical symbols and phrases. The purpose of this work is to …