The optical character recognition (OCR) is the process of converting textual scanned image into a computer editable format. The proposed OCR system is for complex handwritten Kannada characters. One of the major challenges faced by Kannada OCR system is recognition of handwritten text from an image. The input text image is subjected to preprocessing and then converted into binary image. Segment...

Optical neural network (ONNs) are emerging as attractive proposals for machine-learning applications. However, the stability of ONNs decreases with circuit depth, limiting scalability practical uses. Here we demonstrate how to compress depth scale only logarithmically in terms dimension data, leading an exponential gain noise robustness. Our low-depth (LD)-ONN is based on architecture, called C...

Motivated by the non-linear interpolation and generalization abilities of the hybrid optical neural network filter between the reference and non-reference images of the true-class object we designed the modifiedhybrid optical neural network filter. We applied an optical mask to the hybrid optical neural network’s filter input. The mask was built with the constant weight connections of a randoml...

• A type of shallow modules are proposed to directly predict the feature flow for alignment in a single network. Self-supervision learning is introduced further improve quality predicted flow. new state-of-the-art performance shown by comparing with other methods, while fast inference speed maintained. Optical flow, which expresses pixel displacement, widely used many computer vision tasks prov...

The performance of Hopfield's neural net operating in synchronous and asynchronous modes is contrasted. Two interconnect matrices are considered: (1) the original Hopfield interconnect matrix; (2) the original Hopfield interconnect matrix with self-neural feedback. Specific attention is focused on techniques to maximize convergence rates and avoid steady-state oscillation. We identify two oscil...

This paper deals with outlier modeling within a very special framework: a segment-based speech recognizer. The recognizer is built on a neural net that, besides classifying speech segments, has to identify outliers as well. One possibility is to artificially generate outlier samples, but this is tedious, error-prone and significantly increases the training time. This study examines the alternat...