Large-scale Street View Text with Partial Labeling (ICDAR-2019 LSVT)

2019-05-29 (v. 1)
This work is licensed under a: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

Contact author

Yipeng Sun

Baidu Inc, Beijing, China

sunyipeng@baidu.com

13264022002

+86 10 56082834

You can cite this dataset as: Yipeng Sun, Large-scale Street View Text with Partial Labeling (ICDAR-2019 LSVT) ,1,ID:ICDAR-2019 LSVT_1,URL:https://tc11.cvc.uab.es/datasets/ICDAR-2019 LSVT_1

Dataset Information

Dataset URL

http://bjyz-ai.epc.baidu.com/broad/download?dataset=lsvt

Keywords

large-scale street view text, partial annotations

Description

UPDATE: Alternative download is available through the RRC Platform (registration required): https://rrc.cvc.uab.es/?ch=16&com=downloads

 

LSVT consists of 20,000 testing data, 30,000 training data in full annotations and 400,000 training data in weak annotations, which are referred to as partial labels. For most of the training data in weak labels, only one transcription per image is provided, which we refer to as `text-of-interest'. All the images were captured from streets, which consist of a large variety of complicated real-world scenarios, e.g., store fronts and landmarks, making the challenge extreme high by narrowing gaps between research and real applications. 

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