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

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41#
發(fā)表于 2025-3-28 15:46:35 | 只看該作者
Context-Aware Chart Element Detection in the general image domain, chart element detection relies heavily on context information as charts are highly structured data visualization formats. To address this, we propose a novel method ., which stands for .ontext-.ware .art .lement .etection, by integrating a local-global context fusion mo
42#
發(fā)表于 2025-3-28 19:39:52 | 只看該作者
43#
發(fā)表于 2025-3-28 23:16:32 | 只看該作者
44#
發(fā)表于 2025-3-29 03:41:53 | 只看該作者
New Frontiers in Translation Studiesreal-world structure diagrams using the preliminary model and then making few manual corrections. Finally, we experimentally verified the significant performance advantage of our structure diagram recognition method over previous methods.
45#
發(fā)表于 2025-3-29 08:08:33 | 只看該作者
New Frontiers in Translation Studiesocuments, and benchmark it on our novel FIR dataset. Our framework used Encoder-Decoder architecture for localizing and labelling the form fields and for recognizing the handwritten content. The encoder consists of Faster-RCNN and Vision Transformers. Further the Transformer-based decoder architectu
46#
發(fā)表于 2025-3-29 11:43:23 | 只看該作者
Ali Jalalian Daghigh,Mark Shuttleworthd structures are used for better user experience and readability. The resulting model is compact, explainable and end-to-end trainable. The proposed technique outperforms the state-of-the-art algorithms in terms of binarization accuracy and successfully extracted information rates.
47#
發(fā)表于 2025-3-29 15:38:49 | 只看該作者
Cultural Profiling for Translation Purposese-of-the-art in both datasets, achieving a word recognition rate of . and a 2.41 DTW on IRONOFF and an expression recognition rate of . and a DTW of 13.93 on CROHME 2019. This work constitutes an important milestone toward full offline document conversion to online.
48#
發(fā)表于 2025-3-29 22:22:53 | 只看該作者
https://doi.org/10.1007/978-981-13-6343-6our approach, we create character segmentation ground truths for two popular on-line handwriting datasets, IAM-OnDB and HANDS-VNOnDB, and evaluate multiple methods on them, demonstrating that our approach achieves the overall best results.
49#
發(fā)表于 2025-3-30 02:55:59 | 只看該作者
50#
發(fā)表于 2025-3-30 06:16:19 | 只看該作者
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