Recognizing text from degraded and low-resolution document images is still an open challenge in the vision community. Existing text recognition systems require a certain resolution and fails if the document is of low-resolution or heavily degraded or noisy. This paper presents an end-to-end trainable deep-learning based framework for joint optimization of document enhancement and recognition. We are using a generative adversarial network (GAN) based framework to perform image denoising followed by deep back projection network (DBPN) for super-resolution and use these super-resolved features to train a bidirectional long short term memory (BLSTM) with Connectionist Temporal Classification (CTC) for recognition of textual sequences. The entire network is end-to-end trainable and we obtain improved results than state-of-the-art for both the image enhancement and document recognition tasks. We demonstrate results on both printed and handwritten degraded document datasets to show the generalization capability of our proposed robust framework.

With the rapid advancement in internet connectivity, Connected Vehicle network is becoming a promising technology in the current years. However, the increased connectivity most often results in intensifying risk of cyber security threats. In this regard, the network of connected vehicle is also vulnerable to various cyber-attacks. This paper discussed the security issues with connected Priority Vehicle. A priority vehicle must ensure the high level of security otherwise, the results would be devastating and the consequences might be very serious. To ensure the safe and secure movement of connected priority vehicle on the road using blockchain technology. In our proposed system, the priority vehicle movement is controlled by blockchain network, and it only accept messages and signals from blockchain authorized nodes. In particular, the priority vehicle's location, speed and travel time are the important parameters that we secure using blockchain network. This paper also discusses the possible attacks on the priority vehicles and show how our solution can mitigate these attacks. Here it justifies the effectiveness and performance of our security solution using simulation and proves that the blockchain technology shows the potential application towards securing the information in the connected vehicle network.

Knowledge extraction from semi-structured or unstructured documents and texts can be handled by various approaches and viewpoints, including theoretical or mathematical constructions and matrix algebra applications to various ontologies and implementation techniques.