Meta features-based scale invariant OCR decision making using LSTM-RNN

Abstract

Urdu optical character recognition (OCR) is a complex problem due to the nature of its script, which is cursive. Recognizing characters of different font sizes further complicates the problem. In this research, long short term memory-recurrent neural network (LSTM-RNN) and convolution neural network (CNN) are used to recognize Urdu optical characters of different font sizes. LSTM-RNN is trained on formerly extracted feature sets, which are extracted for scale invariant recognition of Urdu characters. From these features, LSTM-RNN extracts meta features. CNN is trained on raw binary images. Two benchmark datasets, i.e. centre for language engineering text images (CLETI) and Urdu printed text images (UPTI) are used. LSTM-RNN reveals consistent results on both datasets, and outperforms CNN. Maximum 99% accuracy is achieved using LSTM-RNN.