UrduText: A Dataset and Benchmark for Urdu Text Detection and Recognition in Natural Scenes Multilingual text in natural scene images conveys useful information and is a fundamental tool for tourists to interact with their environment. Multilingual text detection and recognition in natural scenes, therefore, has become a challenging problem for researchers in the last few years. Recently, a largescale multilingual dataset for scene text detection and script identification is published by the ICDAR which, contains scene images with text in six different scripts including Arabic. This paper presents a novel dataset and benchmark for Urdu text in natural scenes. Currently, no dataset for Urdu text in natural scenes is publicly available. Urdu is a type of cursive language, which is derived from Arabic script and uses many similar alphabet characters. Therefore, the proposed dataset could be helpful for multilingual text detection, recognition and script identification. The aim of this dataset is to help the research community for algorithm development and evaluation of Urdu text in natural scenes. The UrduText dataset contains 1400 complete scene images and 8200segmented words. The images in the dataset contain a broad variety of text instances in multiorientations with small and large font sizes. The dataset contains ground truths in the form of bounding boxes at the word level, the script of the text and the texttranscription. The performance of three deep neural networks is evaluated to measure the robustness of the UrduText dataset.

ICDAR2019 Robust Reading Challenge on Multilingual Scene Text Detection and Recognition xe2x80x94 RRCMLT2019 With the growing cosmopolitan culture of modern cities, the need of robust MultiLingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the stateoftheart forward, the proposed competition builds on top of the RRCMLT2017 with an additional endtoend task, an additional language in the real images dataset, a large scale multilingual synthetic dataset to assist the training, and a baseline EndtoEnd recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multilingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) endtoend detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRCMLT2019 challenge.