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Aug 23, 2020 · Extensive experiments are conducted on public scene text detection datasets including ICDAR 2017 MLT, ICDAR 2015, Total-text and SCUT-CTW1500. The experimental results demonstrate that the proposed method has outperformed previous state-of-the-art methods. Deutsches Forschungszentrum für Künstliche Intelligenz German Research Center for Artificial Intelligence

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Experimental results in the ICDAR’11 and ICDAR’13 datasets demonstrate that our solution yields the best trade-off between effectiveness and efficiency and also achieved the state-of-the-art results in the ICDAR’11 dataset with an f-measure of 96.09%.
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) 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。

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Aug 23, 2020 · Extensive experiments are conducted on public scene text detection datasets including ICDAR 2017 MLT, ICDAR 2015, Total-text and SCUT-CTW1500. The experimental results demonstrate that the proposed method has outperformed previous state-of-the-art methods.
#2 best model for Scene Text Detection on ICDAR 2017 MLT (Precision metric) Browse State-of-the-Art ... DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT See full list on github.com

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这是因为作者用的mlt训练的,他的数据就是这么标注的,如果你要用一些水平文本的数据集,标注是x,y,w,h的,也是可以的,修改一下split_label的代码,或者写个小脚本把x,y,w,h转换成x1,y1,x2,y2,x3,y3,x4,y4就行。
Experimental results on our own dataset, which contains pair of images of different situations, and the standard datasets, namely, ICDAR 2013, MSRATD-500, CTW1500, Total-text, ICDAR 2017 MLT and ... ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script ICDAR2017 Competition on Historical Document Writer Identification (Historical-WI) Competition on Multi-script Writer Identification Using LAMIS-MSHD and CERUG Databases

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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.
What's more, MTRNet achieves state-of-the-art results on several real-world datasets including ICDAR 2013, ICDAR 2017 MLT, and CTW1500, without being explicitly trained on this data, outperforming previous state-of-the-art methods trained directly on these datasets. ICDAR 2015 Icdar2015 is a commonly used dataset for text detection. It contains a total of 1500 pictures, 1000 of which are used for training and the remaining are for testing. The. ICDAR 2017 MLT. ICDAR 2017 MIL is a large scale multi-lingual text dataset, which includes 7200 training im- ages, 1800 validation images and 9000 testing images.

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ICDAR 2017 MLT [ 6] is a multi-lingual text dataset, which is composed of 7,200 training images, 1,800 validation images, and 9,000 testing images. Six different scripts are found at arbitrary position in these images. It, therefore, is a very challenging dataset.
We provide trained model on ICDAR 2017 MLT dataset here and ICDAR 2015 dataset here for downloading. Note that the result is slightly different from we reported in the paper, because PMTD is based on a private codebase, we reimplement inference code based on maskrcnn-benchmark. ICDAR 2017 Finetune with ICDAR 2015, ICDAR 2017 MLT or ICDAR 2013 (if you are using the pre-trained model, place all of the files in ckpt/synthText_10eps/) Combine ICDAR data before training. Place ICDAR data under tmp/ foler. Run the following script to combine the data. python combine_ICDAR_data.py --year [year of ICDAR to train(13 or 15 or 17)]

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Omnidirectional Scene Text Detection with Sequential-free Box Discretization. 06/06/2019 ∙ by Yuliang Liu, et al. ∙ South China University of Technology International Student Union ∙ 0 ∙ share
ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script ICDAR2017 Competition on Historical Document Writer Identification (Historical-WI) Competition on Multi-script Writer Identification Using LAMIS-MSHD and CERUG Databases