Smart Document Analysis Using AI-ML

Abstract

 In this era of digitalization, everything is  smart and digitalized. All the documents are presented,  prepared and shared as soft copies. Classifying those  soft copy documents is gaining an important insight in  recent times. It is attracting digital word with its impact  in different fields like spam filtering, email routing,  language identification, genre classification,  sentimental analysis, readability assessment.  Classifying documents that are available online using  smart techniques helps different business. The easiest  and efficient way of doing it is through machine  learning and it makes human work much easier. To  perform classification of document more statistically,  documents should be given in a much understandable  format to the machine learning classifier. In this report,  I’m discussing the types of feature depending on which  an document can be classified and later represented.  Record arrangement or classifying the documents is the  purpose of document collection and classifications based  upon the information it consists off and features that it  contains. Record arrangement is a huge learning issue  that is at the center of numerous data executives and  recovery. Document grouping plays an important role  in different applications that help with sorting out,  ordering, looking and briefly speaking to a lot of data.  In this report, we will be discussing the uses of  document classification and important steps used for  classifying the document or text by considering a small  use case to know how document classification is done,  basic steps of document classification, processing and  analyzing the documents that are collected. We have  considered two different categories of data sets for  classification and analysis. The problem statement here  is to distinguish those two documents where one is  Rhyme document and each rhyme is taken as a single  file and the other is normal sentences that are a  Non-Rhyme document that contains normal Wikipedia 

text where few statements of Wikipedia is considered as  a single file. The precise objective of my project is to  develop scalable and efficient document classification  project that classifies the document more precisely  depending on the feature that it contains and to know  the basic techniques that are used for the document a  classification like, data collection, data cleaning,  pre-processing and constructing an ML model and  applying the ML algorithm. Another objective of the  project is to work on machine learning concepts and to  get insight into different classification algorithms with  the help of this case study.