Zobrazeno 1 - 10
of 347
pro vyhledávání: '"Nakagawa Masaki"'
Autor:
Dantsuji, Takao, Nakagawa, Masaki
This paper evaluated the effects of the Tokyo 2020 Olympic and Paralympic Games on traffic demand on the Metropolitan expressway. We constructed panel data for both passenger and freight vehicles' demand using longitudinal disaggregated trip records
Externí odkaz:
http://arxiv.org/abs/2409.05296
Autor:
Dantsuji, Takao, Nakagawa, Masaki
Publikováno v:
In Transportation Research Part A January 2025 191
This paper presents an experiment of automatically scoring handwritten descriptive answers in the trial tests for the new Japanese university entrance examination, which were made for about 120,000 examinees in 2017 and 2018. There are about 400,000
Externí odkaz:
http://arxiv.org/abs/2201.03215
Autor:
Truong, Thanh-Nghia, Nguyen, Cuong Tuan, Zanibbi, Richard, Mouchère, Harold, Nakagawa, Masaki
Publikováno v:
In Pattern Recognition September 2024 153
Handwritten mathematical expressions (HMEs) contain ambiguities in their interpretations, even for humans sometimes. Several math symbols are very similar in the writing style, such as dot and comma or 0, O, and o, which is a challenge for HME recogn
Externí odkaz:
http://arxiv.org/abs/2108.05002
In this paper, we propose an RNN-Transducer model for recognizing Japanese and Chinese offline handwritten text line images. As far as we know, it is the first approach that adopts the RNN-Transducer model for offline handwritten text recognition. Th
Externí odkaz:
http://arxiv.org/abs/2106.14459
Toward a computer-assisted marking for descriptive math questions,this paper presents clustering of online handwritten mathematical expressions (OnHMEs) to help human markers to mark them efficiently and reliably. We propose a generative sequence sim
Externí odkaz:
http://arxiv.org/abs/2105.10159
This paper presents a temporal classification method for all three subtasks of symbol segmentation, symbol recognition and relation classification in online handwritten mathematical expressions (HMEs). The classification model is trained by multiple
Externí odkaz:
http://arxiv.org/abs/2105.10156
This paper proposes a method for recognizing online handwritten mathematical expressions (OnHME) by building a symbol relation tree (SRT) directly from a sequence of strokes. A bidirectional recurrent neural network learns from multiple derived paths
Externí odkaz:
http://arxiv.org/abs/2105.06084
Publikováno v:
Pattern Recognition Letters, Volume 121, 2019, Pages 104-112
The text-independent approach to writer identification does not require the writer to write some predetermined text. Previous research on text-independent writer identification has been based on identifying writer-specific features designed by expert
Externí odkaz:
http://arxiv.org/abs/2009.04877