Titlebook: Document Analysis and Recognition - ICDAR 2023; 17th International C Gernot A. Fink,Rajiv Jain,Richard Zanibbi Conference proceedings 2023

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書目名稱Document Analysis and Recognition - ICDAR 2023影響因子(影響力)




書目名稱Document Analysis and Recognition - ICDAR 2023影響因子(影響力)學科排名




書目名稱Document Analysis and Recognition - ICDAR 2023網絡公開度




書目名稱Document Analysis and Recognition - ICDAR 2023網絡公開度學科排名




書目名稱Document Analysis and Recognition - ICDAR 2023被引頻次




書目名稱Document Analysis and Recognition - ICDAR 2023被引頻次學科排名




書目名稱Document Analysis and Recognition - ICDAR 2023年度引用




書目名稱Document Analysis and Recognition - ICDAR 2023年度引用學科排名




書目名稱Document Analysis and Recognition - ICDAR 2023讀者反饋




書目名稱Document Analysis and Recognition - ICDAR 2023讀者反饋學科排名

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Document Analysis and Recognition - ICDAR 2023978-3-031-41685-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Paola Gentile,María Luisa Rodríguez Mu?ozion accuracy and non-character rejection capability. The classifier can be trained on both character samples and string samples but real string samples are usually insufficient. In this paper, we proposed a learning method for segmentation-based online handwritten Chinese text recognition with a con

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Pharmaceutical Industry Performancel NLP models constitutes an intuitive solution. However, due to the difficulty of recognizing handwriting and the error propagation problem, optimized architectures are required. Recognition-free approaches proved to be robust, but often produce poorer results compared to recognition-based methods.

向外

Pharmaceutical Industry Performancepower of language models in this context is the existence of many specialized domains with language statistics very different from those implied by a general language model - think of checks, medical prescriptions, and many other specialized document classes. This paper introduces an algorithm for e

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https://doi.org/10.1007/978-3-319-50042-3sed OCR systems are computationally expensive because they rely on computationally expensive pretraining over text images. To address this challenge, we propose a robust architecture that utilizes a custom CNN block with a Transformer encoder for image understanding and a pre-trained Transformer dec