  Diacritic characters can be considered as a unique set of characters
providing us with adequate and significant clue in identifying a given language
with considerably high accuracy. Diacritics, though associated with phonetics
often serve as a distinguishing feature for many languages especially the ones
with a Latin script. In this proposed work, we aim to identify language of text
in images using the presence of diacritic characters in order to improve
Optical Character Recognition (OCR) performance in any given automated
environment. We showcase our work across 13 Latin languages encompassing 85
diacritic characters. We use an architecture similar to Squeezedet for object
detection of diacritic characters followed by a shallow network to finally
identify the language. OCR systems when accompanied with identified language
parameter tends to produce better results than sole deployment of OCR systems.
The discussed work apart from guaranteeing an improvement in OCR results also
takes on-device (mobile phone) constraints into consideration in terms of model
size and inference time.
