作者：
Zhang, Ting*;Mouchere, Harold;Viard-Gaudin, Christian
期刊：
Neural Computing and Applications,2020年32(9):4689-4708 ISSN：0941-0643
通讯作者：
Zhang, Ting
作者机构：
[Zhang, Ting] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Viard-Gaudin, Christian; Mouchere, Harold] Univ Nantes, CNRS, UMR 6004, LS2N,IPI, Nantes, France.
通讯机构：
[Zhang, Ting] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
摘要：
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 complex data structures as encountered in 2D graphic languages is proposed in this work. Specifically, we address the problem of handwritten mathematical expression recognition, using a tree-based BLSTM architecture allowing the direct labeling of nodes (symbol) and edges (relationship) from a graph modeling the input strokes. One major difference with the traditional approaches is that there is no explicit segmentation, recognition and layout extraction steps but a unique trainable system that produces directly a stroke label graph describing a mathematical expression. The proposed system, considering no grammar, achieves competitive results in online math expression recognition domain.
作者机构：
[Zheng, Lina] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Hubei, Peoples R China.;[Yu, Xinguo; Zhang, Ting] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议名称：
15th IAPR International Conference on Document Analysis and Recognition (ICDAR) / 2nd Workshop of Machine Learning (WML)
会议时间：
SEP 21-22, 2019
会议地点：
Sydney, AUSTRALIA
会议主办单位：
[Zheng, Lina] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Hubei, Peoples R China.^[Zhang, Ting;Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议论文集名称：
Proceedings of the International Conference on Document Analysis and Recognition
关键词：
Handwritten symbol recognition;Chemical organic ring structure symbols;convolutional neural networks
摘要：
Many types of data exhibit characteristic of rotational symmetry. Chemical Organic Ring Structure(ORS) Symbol is such a case. In this paper, we focus on offline handwritten chemical ORS Symbols recognition using convolutional neural networks(CNNs), from application point of view, in order to relax the inconvenience and ineffectiveness of the traditional click-and-drag style of interaction when input chemical notations into electronic devices; from scientific point of view, to explore the capacity of rotation invariance of CNNs using data augmentation. We propose a VGGNet-based classifier for offline handwritten chemical ORS Symbols. To evaluate it, a new dataset of 3600 samples are collected of which 90% is for training while 10% is for test. The recognition accuracy is 84.3% with VGGNet-16 and 92.4% with VGGNet-19.
摘要：
Band overlap and random noise exist widely when the spectra are captured using an infrared spectrometer, especially since the aging of instruments has become a serious problem. In this paper, via introducing the similarity of multiscales, a blind spectral deconvolution method is proposed. Considering that there is a similarity between latent spectra at different scales, it is used as prior knowledge to constrain the estimated latent spectrum similar to pre-scale to reduce artifacts which are produced from deconvolution. The experimental results indicate that the proposed method is able to obtain a better performance than state-of-the-art methods, and to obtain satisfying deconvolution results with fewer artifacts. The recovered infrared spectra can easily extract the spectral features and recognize unknown objects.
作者机构：
[Liu, Tingting; Zhang, Zhaoli; Liu, Sanya; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Yan, Luxin; Zhang, Tianxu] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China.
通讯机构：
[Liu, Hai] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
关键词：
Raman spectroscopy;Optical data processing;Baseline correction;Denoising;Morphological operation;Regularization
摘要：
Laser instruments often suffer from the problem of baseline drift and random noise, which greatly degrade spectral quality. In this article, we propose a variation model that combines baseline correction and denoising. First, to guide the baseline estimation, morphological operations are adopted to extract the characteristics of the degraded spectrum. Second, to suppress noise in both the spectrum and baseline, Tikhonov regularization is introduced. Moreover, we describe an efficient optimization scheme that alternates between the latent spectrum estimation and the baseline correction until convergence. The major novel aspect of the proposed algorithms is the estimation of a smooth spectrum and removal of the baseline simultaneously. Results of a comparison with state-of-the-art methods demonstrate that the proposed method outperforms them in both qualitative and quantitative assessments.
摘要：
Estimating an accuracy spectrum from a linearly degraded and noisy data record is often desirable in infrared spectroscopy application. In this paper, we proposed a new framework which can estimate the desired spectrum and the instrument function simultaneously from the degraded data. Firstly, the instrument function is parametrically represented as a Lorentzian function for the first time. Then, we construct the cost functional with the DE regularization, and minimize the functional to obtain the desired spectrum and instrument function. It has been found that the program is very useful to determine accurate line widths and peak positions from degraded spectral spectrum. Comparative results including quantitative and qualitative analyses manifest that the proposed method outperforms the conventional methods on peak narrowing and noise suppression.
