Neuromorphic acceleration for context aware text image recognition


Although existing optical character recognition (OCR) tools can achieve excellent performance in text image detection and pattern recognition, they usually require a clean input image. Most of them do not perform well when the image is partially occluded or smudged. Humans are able to tolerate much worse image quality during reading because the perception errors can be corrected by the knowledge in word and sentence level context. In this paper, we present a brain-inspired information processing framework for context-aware Intelligent Text Recognition (ITR) and its acceleration using memristor based crossbar array. The ITRS has a bottom layer of massive parallel Brain-state-in-a-box (BSB) engines that give fuzzy pattern matching results and an upper layer of statistical inference based error correction. The framework works robustly in noisy environment. A parallel architecture is presented that incorporates the memristor crossbar array to accelerate the pattern matching. Compared to traditional microprocessor, the accelerator has the potential to provide tremendous area and power savings and more than 8,000 times speedups.