On-Device Language Identification of Text in Images using Diacritic Characters
This work addresses the need for efficient language identification to enhance OCR systems in automated environments, particularly for mobile applications, though it is incremental as it builds on existing object detection methods.
The paper tackles the problem of language identification in text images by detecting diacritic characters to improve OCR performance, achieving high accuracy across 13 Latin languages with 85 diacritic characters while optimizing for on-device constraints like model size and inference time.
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.