Optical Character Recognition (OCR) has become one of the most important techniques in computer vision, given that it can easily obtain information from various images. However, existing OCR techniques cannot recognition Telugu literature characters (Handwritten Golusu Kattu writing) due to a lack of datasets and trained deep Convolutional Neural Networks (CNN). Since the Kakatiya Empire (12th to 14th century) the glorious era of Telugu literature spread across the region. Thereupon, several handwritten documents consist of ancient knowledge, health care tips, wealth information, and several land records written in Telugu Golusukattu writing. Therefore, getting that information has become a major problem because of a lack of expertise in Golusukattu writing skills in skills. In order to solve the above problem, we are proposing deep learning aided-OCR for Telugu literature.

Digital content security gained immense attention over past two decades due rapid digitization of industries and government sectors, and providing security to digital content became a vital challenge. Digital watermarking is one prominent solution to protect digital content from tamper detection and content authentication. However, digital watermarking can alter sensitive information present on cover-content during embedding, then the recovery of exact cover-content may not be possible during extraction process. Moreover, certain applications may not allow small distortions in cover-content. Hence, reversible watermarking techniques of digital content can extract cover-content and watermark completely. Additionally, reversible watermarking is gaining popularity by an increasing number of applications in military, law enforcement, healthcare. In this chapter, the authors compare and contrast the different reversible watermarking techniques with quality and embedding capacity parameters. This survey is essential due to the rapid evolution of reversible watermarking techniques.

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