  Scene text recognition in low-resource Indian languages is challenging
because of complexities like multiple scripts, fonts, text size, and
orientations. In this work, we investigate the power of transfer learning for
all the layers of deep scene text recognition networks from English to two
common Indian languages. We perform experiments on the conventional CRNN model
and STAR-Net to ensure generalisability. To study the effect of change in
different scripts, we initially run our experiments on synthetic word images
rendered using Unicode fonts. We show that the transfer of English models to
simple synthetic datasets of Indian languages is not practical. Instead, we
propose to apply transfer learning techniques among Indian languages due to
similarity in their n-gram distributions and visual features like the vowels
and conjunct characters. We then study the transfer learning among six Indian
languages with varying complexities in fonts and word length statistics. We
also demonstrate that the learned features of the models transferred from other
Indian languages are visually closer (and sometimes even better) to the
individual model features than those transferred from English. We finally set
new benchmarks for scene-text recognition on Hindi, Telugu, and Malayalam
datasets from IIIT-ILST and Bangla dataset from MLT-17 by achieving 6%, 5%, 2%,
and 23% gains in Word Recognition Rates (WRRs) compared to previous works. We
further improve the MLT-17 Bangla results by plugging in a novel correction
BiLSTM into our model. We additionally release a dataset of around 440 scene
images containing 500 Gujarati and 2535 Tamil words. WRRs improve over the
baselines by 8%, 4%, 5%, and 3% on the MLT-19 Hindi and Bangla datasets and the
Gujarati and Tamil datasets.
