  Inspired by the success of Deep Learning based approaches to English scene
text recognition, we pose and benchmark scene text recognition for three Indic
scripts - Devanagari, Telugu and Malayalam. Synthetic word images rendered from
Unicode fonts are used for training the recognition system. And the performance
is bench-marked on a new IIIT-ILST dataset comprising of hundreds of real scene
images containing text in the above mentioned scripts. We use a segmentation
free, hybrid but end-to-end trainable CNN-RNN deep neural network for
transcribing the word images to the corresponding texts. The cropped word
images need not be segmented into the sub-word units and the error is
calculated and backpropagated for the the given word image at once. The network
is trained using CTC loss, which is proven quite effective for
sequence-to-sequence transcription tasks. The CNN layers in the network learn
to extract robust feature representations from word images. The sequence of
features learnt by the convolutional block is transcribed to a sequence of
labels by the RNN+CTC block. The transcription is not bound by word length or a
lexicon and is ideal for Indian languages which are highly inflectional.
IIIT-ILST dataset, synthetic word images dataset and the script used to render
synthetic images are available at
http://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-ilst
