350 research outputs found
DiffusionSTR: Diffusion Model for Scene Text Recognition
No full textThis paper presents Diffusion Model for Scene Text Recognition
(DiffusionSTR), an end-to-end text recognition framework using diffusion models
for recognizing text in the wild. While existing studies have viewed the scene
text recognition task as an image-to-text transformation, we rethought it as a
text-text one under images in a diffusion model. We show for the first time
that the diffusion model can be applied to text recognition. Furthermore,
experimental results on publicly available datasets show that the proposed
method achieves competitive accuracy compared to state-of-the-art methods.Comment: Accepted to ICIP 202
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding
No full textThis paper proposes LayoutLLM, a more flexible document analysis method for
understanding imaged documents. Visually Rich Document Understanding tasks,
such as document image classification and information extraction, have gained
significant attention due to their importance. Existing methods have been
developed to enhance document comprehension by incorporating pre-training
awareness of images, text, and layout structure. However, these methods require
fine-tuning for each task and dataset, and the models are expensive to train
and operate. To overcome this limitation, we propose a new LayoutLLM that
integrates these with large-scale language models (LLMs). By leveraging the
strengths of existing research in document image understanding and LLMs'
superior language understanding capabilities, the proposed model, fine-tuned
with multimodal instruction datasets, performs an understanding of document
images in a single model. Our experiments demonstrate improvement over the
baseline model in various document analysis tasks.Comment: LREC-COLING 202
FA Team at the NTCIR-17 UFO Task
No full textThe FA team participated in the Table Data Extraction (TDE) and Text-to-Table Relationship Extraction (TTRE) task of the NTCIR-17 Understanding of Non-Financial Objects in Financial Reports (UFO). This paper reports our approach to solving the problem and discusses the official results. We successfully utilized various enhancement techniques based on the ELECTRA language model to extract valuable data from tables. Our efforts resulted in an impressive TDE accuracy rate of 93.43\%, positioning us in second place on the Leaderboard rankings. This outstanding achievement is a testament to our proposed approach's effectiveness. In the TTRE task, we proposed the rule-based method to extract meaningful relationships between the text and tables task and confirmed the performance
FA Team at the NTCIR-17 UFO Task
No full textThe FA team participated in the Table Data Extraction (TDE) and Text-to-Table Relationship Extraction (TTRE) task of the NTCIR-17 Understanding of Non-Financial Objects in Financial Reports (UFO). This paper reports our approach to solving the problem and discusses the official results. We successfully utilized various enhancement techniques based on the ELECTRA language model to extract valuable data from tables. Our efforts resulted in an impressive TDE accuracy rate of 93.43\%, positioning us in second place on the Leaderboard rankings. This outstanding achievement is a testament to our proposed approach's effectiveness. In the TTRE task, we proposed the rule-based method to extract meaningful relationships between the text and tables task and confirmed the performance.conference pape
FA Team at the NTCIR-17 UFO Task
No full textThe FA team participated in the Table Data Extraction (TDE) and Text-to-Table
Relationship Extraction (TTRE) tasks of the NTCIR-17 Understanding of
Non-Financial Objects in Financial Reports (UFO). This paper reports our
approach to solving the problems and discusses the official results. We
successfully utilized various enhancement techniques based on the ELECTRA
language model to extract valuable data from tables. Our efforts resulted in an
impressive TDE accuracy rate of 93.43 %, positioning us in second place on the
Leaderboard rankings. This outstanding achievement is a testament to our
proposed approach's effectiveness. In the TTRE task, we proposed the rule-based
method to extract meaningful relationships between the text and tables task and
confirmed the performance.Comment: To be appeared at the NTCIR-17 Conferenc
Improvement of estimation system of construction equipment’s type from field images by combination learning of construction equipment’s parts
No full textEstimation system of construction equipment from field image by combination learning of its parts
No full textTemporal Feature Enhancement Network with External Memory for Object Detection in Surveillance Video
No full textCorrection to: Comparison of glycyrrhizin content in 25 major kinds of Kampo extracts containing Glycyrrhizae Radix used clinically in Japan
No full textThe article Comparison of glycyrrhizin content in 25 major kinds of Kampo extracts containing Glycyrrhizae Radix used clinically in Japan, written by Mitsuhiko Nose, Momoka Tada, Rika Kojima, Kumiko Nagata, Shinsuke Hisaka, Sayaka Masada, Masato Homma and Takashi Hakamatsuka, was originally published Online First without open access. After publication in volume 71, issue 4, page 711–722 the author decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed to © The Author(s) 2018 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.</jats:p
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