  Regional language extraction from a natural scene image is always a
challenging proposition due to its dependence on the text information extracted
from Image. Text Extraction on the other hand varies on different lighting
condition, arbitrary orientation, inadequate text information, heavy background
influence over text and change of text appearance. This paper presents a novel
unified method for tackling the above challenges. The proposed work uses an
image correction and segmentation technique on the existing Text Detection
Pipeline an Efficient and Accurate Scene Text Detector (EAST). EAST uses
standard PVAnet architecture to select features and non maximal suppression to
detect text from image. Text recognition is done using combined architecture of
MaxOut convolution neural network (CNN) and Bidirectional long short term
memory (LSTM) network. After recognizing text using the Deep Learning based
approach, the native Languages are translated to English and tokenized using
standard Text Tokenizers. The tokens that very likely represent a location is
used to find the Global Positioning System (GPS) coordinates of the location
and subsequently the regional languages spoken in that location is extracted.
The proposed method is tested on a self generated dataset collected from
Government of India dataset and experimented on Standard Dataset to evaluate
the performance of the proposed technique. Comparative study with a few
state-of-the-art methods on text detection, recognition and extraction of
regional language from images shows that the proposed method outperforms the
existing methods.
