Suparna Saha Biswas, Himadri Mukherjee, Ankita Dhar, Obaidullah Sk Md, Kaushik Roy
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A survey on artificial intelligence-based approaches for personality analysis from handwritten documents
Human personality is a blend of different traits and virtues. It’s modeling is challenging due to its inherent complexity. There are multitudinous cues to predict personality and handwriting is one of them. This is because it is distinctive to a large extent and varies at the individual level. The allied field of science which deals with the analysis of handwriting for understanding personality is known as Graphology. Researchers have discovered disparate features of handwriting that can reveal the personality traits of an individual. Several attempts have been made to model personality from handwriting in different languages but significant advancement is required for commercialization. In this paper, we present the reported aspects of handwriting, techniques for processing handwritten documents and evaluation measures for personality identification to draw a horizon and aid in further advancement of research in this field.
期刊介绍：
The large number of existing documents and the production of a multitude of new ones every year raise important issues in efficient handling, retrieval and storage of these documents and the information which they contain. This has led to the emergence of new research domains dealing with the recognition by computers of the constituent elements of documents - including characters, symbols, text, lines, graphics, images, handwriting, signatures, etc. In addition, these new domains deal with automatic analyses of the overall physical and logical structures of documents, with the ultimate objective of a high-level understanding of their semantic content. We have also seen renewed interest in optical character recognition (OCR) and handwriting recognition during the last decade. Document analysis and recognition are obviously the next stage.
Automatic, intelligent processing of documents is at the intersections of many fields of research, especially of computer vision, image analysis, pattern recognition and artificial intelligence, as well as studies on reading, handwriting and linguistics. Although quality document related publications continue to appear in journals dedicated to these domains, the community will benefit from having this journal as a focal point for archival literature dedicated to document analysis and recognition.
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