TITLE: Urdu text in natural scene images: a new dataset and preliminary text detection 

ABSTRACT: Text detection in natural scene images for content analysis is an interesting
task. The research community has seen some great developments for
English/Mandarin text detection. However, Urdu text extraction in natural scene
images is a task not well addressed. In this work, firstly, a new dataset is
introduced for Urdu text in natural scene images. The dataset comprises of 500
standalone images acquired from real scenes. Secondly, the channel enhanced
Maximally Stable Extremal Region (MSER) method is applied to extract Urdu text
regions as candidates in an image. Two-stage filtering mechanism is applied to
eliminate non-candidate regions. In the first stage, text and noise are
classified based on their geometric properties. In the second stage, a support
vector machine classifier is trained to discard non-text candidate regions.
After this, text candidate regions are linked using centroid-based vertical and
horizontal distances. Text lines are further analyzed by a different classifier
based on HOG features to remove non-text regions. Extensive experimentation is
performed on the locally developed dataset to evaluate the performance. The
experimental results show good performance on test set images. The dataset will
be made available for research use. To the best of our knowledge, the work is
the first of its kind for the Urdu language and would provide a good dataset
for free research use and serve as a baseline performance on the task of Urdu
text extraction.
Text detection in natural scene images for content analysis is an interesting
task. The research community has seen some great developments for
English/Mandarin text detection. However, Urdu text extraction in natural scene
images is a task not well addressed. In this work, firstly, a new dataset is
introduced for Urdu text in natural scene images. The dataset comprises of 500
standalone images acquired from real scenes. Secondly, the channel enhanced
Maximally Stable Extremal Region (MSER) method is applied to extract Urdu text
regions as candidates in an image. Two-stage filtering mechanism is applied to
eliminate non-candidate regions. In the first stage, text and noise are
classified based on their geometric properties. In the second stage, a support
vector machine classifier is trained to discard non-text candidate regions.
After this, text candidate regions are linked using centroid-based vertical and
horizontal distances. Text lines are further analyzed by a different classifier
based on HOG features to remove non-text regions. Extensive experimentation is
performed on the locally developed dataset to evaluate the performance. The
experimental results show good performance on test set images. The dataset will
be made available for research use. To the best of our knowledge, the work is
the first of its kind for the Urdu language and would provide a good dataset
for free research use and serve as a baseline performance on the task of Urdu
text extraction.
Urdu text in natural scene images: a new dataset and preliminary text detection
http://arxiv.org/abs/2109.08060v1