Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDAR.2019.00098 | 2019 International Conference on Document Analysis and Recognition (ICDAR) |
Keywords | Field | DocType |
Handwriting Recognition,Chemical Formulae Recognition,Recurrent Neural Networks | Chemical formula,Chemical equation,Pattern recognition,Computer science,End-to-end principle,Symbol (chemistry),Handwriting recognition,Recurrent neural network,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
1520-5363 | 978-1-7281-3015-6 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoxue Liu | 1 | 0 | 0.34 |
| Ting Zhang | 2 | 0 | 2.03 |
| Xinguo Yu | 3 | 0 | 0.68 |