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Documentary analysis (also document analysis) is a type of qualitative research in which documents are reviewed by the analyst to assess an appraisal theme. Dissecting documents involves coding content into subjects like how focus group or interview transcripts are investigated. A rubric can likewise be utilized to review or score a document ...
A style guide, or style manual, is a set of standards for the writing and design of documents, either for general use or for a specific publication, organization or field. The implementation of a style guide provides uniformity in style and formatting within a document and across multiple documents.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Document_analysis&oldid=772876470"This page was last edited on 29 March 2017, at 21:40
It is about character and symbol recognition, printed/handwritten text recognition, graphics analysis and recognition, document analysis, document understanding, historical documents and digital libraries, document based forensics, camera and video based scene text analysis. [1]
Document comparison, also known as redlining or blacklining, is a computer process by which changes are identified between two versions of the same document for the purposes of document editing and review. Document comparison is a common task in the legal and financial industries.
The framework helps to direct the search for deep knowledge, providing structure to the document analysis process, particularly for the domain novice. While the output may initially appear overbearing, its value to the analysis cannot be overstated. The abstraction hierarchy defines the systemic constraints at the highest level.
A good architecture document is short on details but thick on explanation. It may suggest approaches for lower level design, but leave the actual exploration trade studies to other documents. Another type of design document is the comparison document, or trade study. This would often take the form of a whitepaper. It focuses on one specific ...
Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial. Historically in machine learning spatial data was analyzed using a convolutional neural network , and temporal data using a recurrent neural network .