ICDAR预测结果评估指标检测工具. 2018-10-31. ICDAR预测结果评估指标检测工具,用于ICDAR2013场景文字预测结果评估,将预测结果打包放进文件夹里面,详情参考readme.txt,也可以改成自己的数据标签。
The challenges of shape robust text detection lie in two aspects: 1) most existing quadrangular bounding box based detectors are difficult to locate texts with arbitrary shapes, which are hard to be enclosed perfectly in a rectangle; 2) most pixel-wise segmentation-based detectors may not separate the text instances that are very close to each other.
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icdar 2017 mlt 它包含7,200个训练自然场景图像、1,800个验证自然场景图像和9,000个测试自然场景图像,包含6种不同语言的文本(拉丁语、阿拉伯语、孟加拉语、韩语、平假名、片假名和符号)。

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  • ICDAR 2015 and ICDAR 2017 MLT validate the effective-ness of PSENet. Notably, on CTW1500, a dataset full of long curve texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure (82.2%) outperforms state-Authors contributed equally. yXiang Li is with PCA Lab, Key Lab of Intelligent Perception and
  • (2017) Won ICDAR RRC-MLT, competition on cropped word script identification. (2017) Dean's Research Award, IIIT ... the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline ...

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ICDAR 2017 MLT ICDAR 2017 MIL is a large scale multi-lingual text dataset, which includes 7200 training images, 1800 validation images and 9000 testing images. The dataset is composed of complete scene images which come from 9 languages. 6、Experiment Results. Implementation Details

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  • Since then, several larger and more challenging quadrilateral labeled datasets have emerged in the ICDAR 2017 competition, such as RCTW-17 (dataset for Chinese and English text), DOST (scene texts observed by video in the real environment) and MLT (dataset for multi-lingual text). These are quickly becoming the new mainstream datasets.
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ICDAR2017 Robust Reading Challenge on Multi-lingual Scene Text Detection and Script Identification – RRC-MLT: Nibal Nayef, Fei Yin, Imen Bizid, Hyunsoo Choi, Yuan Feng, Dimosthenis Karatzas, Zhenbo Luo, Umapada Pal, Christophe Rigaud, Joseph Chazalon, Wafa Khlif, Muhammad Muzzamil Luqman, Jean-Christophe Burie, Cheng-lin Liu, Jean-Marc Ogier: C1-2

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Jun 07, 2018 · The state-of-the-art results on ICDAR 2015 and ICDAR 2017 MLT benchmarks further confirm the great effectiveness of PSENet. Notably, PSENet outperforms the previous best record by absolute 6.37\% on the curve text dataset SCUT-CTW1500. Code will be available in this https URL

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(2018) Best Paper Award, to E2E-MLT at IWRR ACCV. (2017) Open Informatics Young Scientist Scholarship, Czech Technical University in Prague. (2017) Won ICDAR RRC-MLT, competition on cropped word script identification. (2017) Dean's Research Award, IIIT Hyderabad, for excellence in research.

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Scene Text Recognition Resources. Author: 陈晓雪. Updates. Dec 24, 2019: add 20 papers and update corresponding tables. Feb 29, 2020: add AAAI-2020 papers and update corresponding tables.

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icdar 2017 mlt ICDAR 2017 MLT5 旨在对多语言场景下的文本检测和识别任务进行基准测试。 它包含7,200个训练自然场景图像、1,800个验证自然场景图像和9,000个测试自然场景图像,包含6种不同语言的文本(拉丁语、阿拉伯语、孟加拉语、韩语、平假名、片假名和符号)。

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dataset:ICDAR 2017 MLT 📚 Many Task Learning (MaTL) , 利用了多任务路由层,将多种任务综合到统一模型中去。 (from 阿姆斯特丹大学)

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