Titlebook: Document Analysis and Recognition - ICDAR 2024; 18th International C Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng Conference proceedi

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書(shū)目名稱(chēng)Document Analysis and Recognition - ICDAR 2024影響因子(影響力)




書(shū)目名稱(chēng)Document Analysis and Recognition - ICDAR 2024影響因子(影響力)學(xué)科排名




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書(shū)目名稱(chēng)Document Analysis and Recognition - ICDAR 2024網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




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書(shū)目名稱(chēng)Document Analysis and Recognition - ICDAR 2024讀者反饋




書(shū)目名稱(chēng)Document Analysis and Recognition - ICDAR 2024讀者反饋學(xué)科排名

規(guī)章

細(xì)節(jié)

Document Specular Highlight Removal with?Coarse-to-Fine Strategyetween the ground-truth and the CP-predicted image. Experimental results on four public benchmark images demonstrate that our method surpasses state-of-the-art methods in the task of highlight removal.

WITH

KVP10k : A Comprehensive Dataset for?Key-Value Pair Extraction in?Business Documents0k , a new dataset and benchmark specifically designed for KVP extraction. The dataset contains 10707richly annotated images. In our benchmark, we also introduce a new challenging task that combines elements of KIE as well as KVP in a single task. KVP10k sets itself apart with its extensive diversit

Nutrient

Context-Aware Confidence Estimation for?Rejection in?Handwritten Chinese Text Recognitionand binary geometric features. Experimental evaluations on the CASIA-HWDB and ICDAR2013 datasets demonstrate that our method can significantly improve the rejection performance in respect of low error rate at moderate rejection rate. The re-trained classifier, the linguistic context and the geometri

Cosmopolitan

Radical Similarity Based Model Optimization and?Post-correction for?Chinese Character Recognitionhe potential error recognition results, offering a low-cost yet effective solution. Experimental results on different radical-based CCR models and datasets demonstrate the effectiveness and robustness of our proposed method.

Cosmopolitan

Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with?Radical Reconstruction promising insights, underscoring the potential and effectiveness of our approach in deciphering the intricacies of ancient Chinese scripts. Through this novel dataset and methodology, we aim to bridge the gap between traditional philology and modern document analysis techniques, offering new insigh

esculent

絆住

GraphMLLM: A Graph-Based Multi-level Layout Language-Independent Model for?Document Understandingmprove the performance of language-independent document pre-trained model. Experimental results show that compared with previous state-of-the-art methods, GraphMLLM yields higher performance on downstream visual information extraction (VIE) tasks after pre-training on less documents. Code and model

Directed

EntityLayout: Entity-Level Pre-training Language Model for?Semantic Entity Recognition and?Relation ental results on public datasets FUNSD and CORD demonstrate that the proposed EntityLayout achieves competitive performance in SER and state-of-the-art performance in RE, i.e., SER F1 scores of 0.9108 and 0.9650, respectively, RE F1 scores of 0.8212 and 0.9898, respectively.