Titlebook: Document Analysis and Recognition – ICDAR 2023 Workshops; San José, CA, USA, A Mickael Coustaty,Alicia Fornés Conference proceedings 2023 T

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




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

B-cell

Leveraging Knowledge Graph Embeddings to?Enhance Contextual Representations for?Relation Extractionpresentation. We conducted a series of experiments which revealed promising and very interesting results for our proposed approach. The obtained results demonstrated an outperformance of our method compared to context-based relation extraction models.

Aesthete

Extracting Key-Value Pairs in?Business Documentstraction in business documents. Our approach is designed to be adaptable and requires minimal semantic and language-specific knowledge, making it suitable for a wide range of business documents. This flexibility allows our method to be easily applied to real-world scenarios, where documents may vary

formula

ENDOW

Subgraph-Induced Extraction Technique for?Information (SETI) from?Administrative Documentsraph level and compare the results with baselines on private as well as public datasets. Our model succeeds in improving recall and precision scores for some classes in our private dataset and produces comparable results for public datasets designed for Form Understanding and Information Extraction.

斗爭

斗爭

狂熱語言

A Comparison of Demographic Attributes Detection from Handwriting Based on Traditional and Deep Learct the demographical attributes of writers. In the deep learning method, a Convolutional Neural Network model based on the ResNet architecture with a fully connected layer, followed by a softmax layer is used to provide probability scores to facilitate demographic information detection. To evaluate

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