Offline Extraction of Indic Regional Language from Natural Scene Image Using Text Segmentation and Deep Convolutional Sequence

Abstract

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 the 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 are 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. A 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.