An EndtoEnd Trainable System for Offline Handwritten Chemical Formulae Recognition In this paper, we propose an endtoend 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 peopleu0027s writing habits, at the same time could help to develop methods for the complicated chemical equations recognition. The proposed system adopts the CNNRNNCTC framework, which is one of state of the art methods in imagebased sequence labelling tasks. We extend the capability of the CNNRNNCTC framework to interpret 2D spatial relationships (such as u0027subscriptu0027 existing in chemical formula) by introducing additional labels to represent them. The system evaluated on a selfcollected data set of 12,224 samples, achieves the recognition rate of 94.98% at the chemical formula level.

Residual BiRNN Based Seq2Seq Model with Transition Probability Matrix for Online Handwritten Mathematical Expression Recognition In this paper, we present a Seq2Seq model for online handwritten mathematical expression recognition (OHMER), which consists of two major parts: a residual bidirectional RNN (BiRNN) based encoder that takes handwritten traces as the input and a transition probability matrix introduced decoder that generates LaTeX notations. We employ residual connection in the BiRNN layers to improve feature extraction. Markovian transition probability matrix is introduced in decoder and longterm information can be used in each decoding step through joint probability. Furthermore, we analyze the impact of the novel encoder and transition probability matrix through several specific instances. Experimental results on the CROHME 2014 and CROHME 2016 competition tasks show that our model outperforms the previous stateoftheart single model by only using the official training dataset.