Arbitrary-Shaped Text (ICDAR-2019 ArT)
Scene Text Recognition
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
The main objective of this task is to recognize every character in a cropped image patch, which is also one of the common tasks in previous RRC. Considering the fact that the research in Chinese scripts text recognition is relatively immature compared to Latin scripts, we decided to further break down T2 into two subcategories: T2.1 - Latin script only; T2.2 - Latin and Chinese scripts.
For T2.1, case-insensitive word accuracy will be taken as the primary challenge metric. Apart from this, all the standard practice for text spotting evaluation such as i) for the ground truth that contain symbols, we will consider symbols in the middle of words, ii) but remove the symbols ( !?.:,*"()·/'_ ) at the beginning and at the end of both the ground truths and the submissions.
For T2.2, we adopt the Normalized Edit Distance metric (1-N.E.D specifically) and case-insensitive word accuracy. 1-N.E.D is also used in the ICDAR 2017 competition ICPR-MTWI . Only the 1-N.E.D will be treated as the official ranking metric.
The expected output is a string of predicted characters.
 Shi, Baoguang, et al. "ICDAR2017 competition on reading chinese text in the wild (RCTW-17)." Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. Vol. 1. IEEE, 2017.
No comments on this dataset yet.