Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning最新文献_第10页

TIRec: Transformer-based Invoice Text Recognition 基于变压器的发票文本识别

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590034

Yanlan Chen

{"title":"TIRec: Transformer-based Invoice Text Recognition","authors":"Yanlan Chen","doi":"10.1145/3590003.3590034","DOIUrl":"https://doi.org/10.1145/3590003.3590034","url":null,"abstract":"A novel invoice text recognition model is proposed. In the past few years, researchers have explored text recognition methods with RNN-like structures to model semantic information. However, RNN-based approaches have some obvious drawbacks, such as the level-by-level decoding approach and the one-way serial transmission of semantic information, which greatly limit semantic information's effectiveness and computational efficiency. In contrast, invoice text has obvious contextual relationships due to its fixed text pattern, the text font in the invoice is more fixed and the complexity of the background is much lower than that of natural scenes. To further exploit these contextual relationships and adapt to the characteristics of invoice text, we propose a new text recognition framework inspired by Transformer [1]. Self-attention-based architectures, in particular Transformer, have been successful in natural language processing (NLP). It has demonstrated powerful semantic information modeling capabilities in NLP. Inspired by its success, we try to apply Transformer to invoice text recognition. Unlike the RNN-based approach, we reduce the parameters of the vision network used to extract image features, use the Convolutional Vision Transformer Attention module to capture the semantic information, and use the Transformer decoding module to decode all characters in parallel. We hope that this Transformer-based architecture can better model the semantic information in invoices while remaining lightweight. Meanwhile, we collected text images of more than 40,000 train invoices, VAT invoices, rolled invoices, and cab invoices. Experiments on the collected invoice text recognition dataset show that our approach outperforms previous methods in terms of accuracy and speed.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125562497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Foreign object recognition method of transmission line based on improved outlier rate method 基于改进离群率法的传输线异物识别方法

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590073

Dongmei Liu, Zhongwang Zhu, Bo Chen

引用次数: 0

PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network PhyGNNet:利用物理信息图神经网络求解时空偏微分方程

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2022-08-07 DOI: 10.1145/3590003.3590029

Longxiang Jiang, Liyuan Wang, Xinkun Chu, Yonghao Xiao, Hao Zhang

{"title":"PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network","authors":"Longxiang Jiang, Liyuan Wang, Xinkun Chu, Yonghao Xiao, Hao Zhang","doi":"10.1145/3590003.3590029","DOIUrl":"https://doi.org/10.1145/3590003.3590029","url":null,"abstract":"Partial differential equations (PDEs) are a common means of describing physical processes. Solving PDEs can obtain simulated results of physical evolution. Currently, the mainstream neural network method is to minimize the loss of PDEs thus constraining neural networks to fit the solution mappings. By the implementation of differentiation, the methods can be divided into PINN methods based on automatic differentiation and other methods based on discrete differentiation. PINN methods rely on automatic backpropagation, and the computation step is time-consuming, for iterative training, the complexity of the neural network and the number of collocation points are limited to a small condition, thus abating accuracy. The discrete differentiation is more efficient in computation, following the regular computational domain assumption. However, in practice, the assumption does not necessarily hold. In this paper, we propose a PhyGNNet method to solve PDEs based on graph neural network and discrete differentiation on irregular domain. Meanwhile, to verify the validity of the method, we solve Burgers equation and conduct a numerical comparison with PINN. The results show that the proposed method performs better both in fit ability and time extrapolation than PINN. Code is available at https://github.com/echowve/phygnnet.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127997209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning 2023第二届亚洲算法、计算与机器学习会议论文集

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 1900-01-01 DOI: 10.1145/3590003

引用次数: 0