Fusion of Spatio-temporal Information for Indic Word Recognition Combining Online and Offline Text Data

Abstract

We present a novel Indic handwritten word recognition scheme by fusion of spatio-temporal information extracted from handwritten images. The main challenge in Indic word recognition lies in its complexity because of modifiers, touching characters, and compound characters. Hidden Markov Models (HMMs) are being used to model such data due to their ability to learn sequential data, however, the recognition performance is not satisfactory. We propose here a Long Short-Term Memory (LSTM)-based architecture for offline Indic word recognition. Offline recognition methods usually involve spatial data, whereas it has been observed that online recognition schemes show better performance than the offline methodologies. Online information usually refers to the temporal information obtained from the strokes of the pen tip while writing, which is missing in offline word images. In this article, an effort has been made to extract the online temporal information from offline images using stroke recovery and later it is combined with spatial information in LSTM architecture. During recognition, the character models are trained using both offline and extracted pseudo-online handwritten data separately. Finally, a novel fusion scheme has been used to combine them together. From the experiment, it is noted that recognition performance of handwritten Indic words improves considerably due to the fusion scheme of spatial and temporal data.