  Recent research in the field of text localization in a resource constrained
environment has made extensive use of deep neural networks. Scene text
localization and recognition on low-memory mobile devices have a wide range of
applications including content extraction, image categorization and keyword
based image search. For text recognition of multi-lingual localized text, the
OCR systems require prior knowledge of the script of each text instance. This
leads to word script identification being an essential step for text
recognition. Most existing methods treat text localization, script
identification and text recognition as three separate tasks. This makes script
identification an overhead in the recognition pipeline. To reduce this
overhead, we propose TeLCoS: OnDevice Text Localization with Clustering of
Script, a multi-task dual branch lightweight CNN network that performs
real-time on device Text Localization and High-level Script Clustering
simultaneously. The network drastically reduces the number of calls to a
separate script identification module, by grouping and identifying some majorly
used scripts through a single feed-forward pass over the localization network.
We also introduce a novel structural similarity based channel pruning mechanism
to build an efficient network with only 1.15M parameters. Experiments on
benchmark datasets suggest that our method achieves state-of-the-art
performance, with execution latency of 60 ms for the entire pipeline on the
Exynos 990 chipset device.
