发布时间：

2026-06-30

所属单位：

人工智能教育学部

论文名称：

An end-to-end trainable system for offline handwritten chemical formulae recognition

发表刊物：

Proceedings of the International Conference on Document Analysis and Recognition. International Conference on Document Analysis and Recognition

刊物所在地：

国内外公开发行

关键字：

Character recognition ; Computer vision

摘要：

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. © 2019 IEEE.

第一作者：

Liu, Xiaoxue

论文类型：

会议论文

通讯作者：

张婷,余新国

论文编号：

1571043918219448320

文献类型：

00013

页面范围：

577-582

ISSN号：

1520-5363

是否译文：

否

发表时间：

2019-09-25

收录刊物：

其他（ESI、Scopus、OA）、EI