  The importance of Scene Text Recognition (STR) in today's increasingly
digital world cannot be overstated. Given the significance of STR, data
intensive deep learning approaches that auto-learn feature mappings have
primarily driven the development of STR solutions. Several benchmark datasets
and substantial work on deep learning models are available for Latin languages
to meet this need. On more complex, syntactically and semantically, Indian
languages spoken and read by 1.3 billion people, there is less work and
datasets available. This paper aims to address the Indian space's lack of a
comprehensive dataset by proposing the largest and most comprehensive real
dataset - IndicSTR12 - and benchmarking STR performance on 12 major Indian
languages. A few works have addressed the same issue, but to the best of our
knowledge, they focused on a small number of Indian languages. The size and
complexity of the proposed dataset are comparable to those of existing Latin
contemporaries, while its multilingualism will catalyse the development of
robust text detection and recognition models. It was created specifically for a
group of related languages with different scripts. The dataset contains over
27000 word-images gathered from various natural scenes, with over 1000
word-images for each language. Unlike previous datasets, the images cover a
broader range of realistic conditions, including blur, illumination changes,
occlusion, non-iconic texts, low resolution, perspective text etc. Along with
the new dataset, we provide a high-performing baseline on three models -
PARSeq, CRNN, and STARNet.
