No abstract

The conversion of large collections of documents from paper to digital formats that are suitable for electronic archival is a complex multi-phase process. The creation of good quality images from paper documents is just one phase. To extract relevant information that they contain, with an accuracy that fits the purpose of target applications, an automated document analysis system and a manual verification/review process are needed. The automated system needs to perform a variety of analysis and recognition tasks in order to reach an accuracy level that minimizes the manual correction effort downstream.This paper describes the complete process and the associated technologies, tools, and systems needed for the conversion of a large collection of complex documents and deployment for online web access to its information rich content. We used this process to recapture 80 years of Time magazines. The historical collection is scanned, automatically processed by advanced document analysis components to extract articles, manually verified for accuracy, and converted in a form suitable for web access. We discuss the major phases of the conversion lifecycle and the technology developed and tools used for each phase. We also discuss results in terms of recognition accuracy.

The process of creating digital archive from paper based document is gaining popularity. Automated systems/frameworks for document analysis techniques have been developed, but still lack in achieving the required accuracy goals in terms of text, article identification etc. Rendering problems, such as missing graphical components, wrong reading ordering in multi columned journals/magazine, missing indentation and broken text lines, hyphenation issues, are basically due to poor layout information extracted from the scanned document during the OCR process. Also lacking are the tools to take the output of these processes and be able to create highly accurate content with associated metadata from the original. The term "Ground Truth" in the current context is used to refer to the process (automatic and manual collectively) by which we ensure that the end result of the process are highly accurate and complete rich text content (articles, papers, etc) generated from the original scanned version of content.We present to the audience PerfectDoc -A suite of tools for manual GroundTruthing. The suite consist of tools to create highly accurate GroundTruth, GT editors and tools to take this data and deliver output suitable for web based viewing.

Human Resource Information technology is a software solution for small to mid-sized businesses to help automate and manage their HR, payroll, management and accounting, recruiting selecting and many others. In the present time the role of IT in HRM is very wide and special An IT in HRM generally should provide the capability to more effectively plan, control and manage HR costs; achieve improved efficiency and quality in HR decision making; and improve employee and managerial productivity and effectiveness. An IT in HRM offers HR, payroll, benefits, training, recruiting and compliance solutions Most are flexibly designed with integrated databases, a comprehensive array of features, and powerful reporting functions and analysis capabilities that you need to manage your workforce. This can give back hours of the HR administrator’s day previously spent attending to routine employee requests. An IT in HRM  also facilitates communication processes and saves paper by providing an easily-accessible, centralized location for company policies, announcements, and links to external URL’s. Employee activities such as time-off requests and W-4 form changes can be automated, resulting in faster approvals and less paperwork. An affordable Human Resource Information System allows companies to manage their workforce through two powerful main components: HR  & Payroll. In addition to these essential software solutions, HRIS offers other options to help companies understand and fully utilize their workforce’s collective skills, talents, and experiences.

In order to simulate human intellect in robots, a vast and interdisciplinary discipline known as artificial intelligence (AI) integrates computer science, data analysis, and problem-solving strategies. It entails the creation of algorithms and systems that allow computers to observe, comprehend, reason, pick up new information, and make decisions. Fundamentally, AI strives to build intelligent computers that can work independently, adapt to new circumstances, and display traits that resemble human intelligence. Machine learning and deep learning are two important subfields and methodologies within the umbrella of artificial intelligence (AI). The main goal of machine learning is to create algorithms that let computers learn from data and get better at what they do without being explicitly taught. Deep learning, a subset of machine learning, processes and analyses complicated data using artificial neural networks modelled after the structure and operation of the human brain. AI's main objective is to create machines that can carry out tasks that have historically required human intelligence, like speech and picture recognition, natural language processing, decision-making, problem-solving, and even creative activities. These systems use enormous databases and computational capacity to do amazing feats as they attempt to comprehend, analyse, and respond to the complexity of the world. AI algorithms can find patterns, connections, and insights by analysing and processing enormous amounts of data that may escape human observers. Significant improvements in various fields, such as business, education, finance, and social media, have been made possible by this skill. By automating procedures, improving judgment, personalizing interactions, and gleaning essential insights from vast amounts of data, AI is revolutionizing various businesses.