An End-to-End Trainable System for Offline Handwritten Chemical Formulae Recognition

Abstract

In this paper, we propose an end-to-end trainable system for recognizing handwritten chemical formulae. This system recognize once a time a chemical formula, instead of one chemical symbol or a whole chemical equation, which is in line with people s writing habits, at the same time could help to develop methods for the complicated chemical equations recognition. The proposed system adopts the CNN+RNN+CTC framework, which is one of state of the art methods in imagebased sequence labelling tasks. We extend the capability of the CNN+RNN+CTC framework to interpret 2D spatial relationships (such as subscript existing in chemical formula) by introducing additional labels to represent them. The system evaluated on a self-collected data set of 12,224 samples, achieves the recognition rate of 94.98% at the chemical formula level.