Arbitrary-Shaped Text (ICDAR-2019 ArT)

Ground Truth

Transcription for the ArT dataset

2019-05-29 (v. 1)

Contact author

Yipeng Sun

Baidu Inc, Beijing, China

sunyipeng@baidu.com

13264022002

+86 10 56082834


This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

Keywords

Description

Task 1 and 3

We create a single JSON file that covers all images in the dataset to store the ground truth in a structured format, following the naming convention:

gt_[image_id]where image_id refers to the index of the image in the dataset.

In the JSON file, each gt_[image_id] corresponds to a list, where each line in the list correspond to one word in the image and gives its bounding box coordinates, transcription, language type and difficulty flag, in the following format:

{

“gt_1”:  [

{“points”: [[x1, y1], [x2, y2], …, [xn, yn]], “transcription” : “trans1”, “language” : “Latin”, "illegibility": false },

{“points”: [[x1, y1], [x2, y2], …, [xn, yn]], “transcription” : “trans2”, “language” : “Chinese”, "illegibility": false }],

“gt_2”:  [

{“points”: [[x1, y1], [x2, y2], …, [xn, yn]] , “transcription” : “trans3”, “language” : “Latin”, "illegibility": false }],

……

}

where x1, y1, x2, y2, …, xn, yn in “points” are the coordinates of the polygon bounding boxes, which could be 4, 8, 10, 12 polygon vertices. The “transcription” denotes the text of each text line, the “language” denotes the language type of the transcription, which could be “Latin” and “Chinese”. Similar to COCOtext [1] and ICDAR2015 [2], “illegibility” represents “Do Not Care” text region when it’s set “true”, which does not influence the results.

Task 2

The given input will be the cropped image patches with corresponding text instances, and the relative polygon spatial coordinates. Similar to Task 1, for all images in the dataset, we create a single JSON file to store the ground truths in a structured format, following the naming convention:

gt_[image_id]where image_id refers to the index of the image in the dataset.

{

“gt_1”:  [{“points”: [[x1, y1], [x2, y2], …, [xn, yn]], “transcription” : “trans1”, “language” : “Latin”, "illegibility": false }],

“gt_3”:  [{“points”: [[x1, y1], [x2, y2], …, [xn, yn]] , “transcription” : “trans2”, “language” : “Latin”, "illegibility": false }],

“gt_3”:  [{“points”: [[x1, y1], [x2, y2], …, [xn, yn]] , “transcription” : “trans3”, “language” : “Latin”, "illegibility": false }],

……

}

The ArT dataset can be download at: http://bjyz-ai.epc.baidu.com/broad/download?dataset=art

[1] Gomez, Raul, et al. "ICDAR2017 robust reading challenge on COCO-Text." 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017.

[2] Karatzas, Dimosthenis, et al. "ICDAR 2015 competition on robust reading." 13th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015.

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