Smart Document Analysis Using AI-ML
Journal Title: International Journal of Innovative Research in Computer Science and Technology - Year 2019, Vol 7, Issue 3
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.
Authors and Affiliations
Sindhu Rashmi. H. R, Prof. Anisha. B. S, Dr. Ramakanth Kumar. P
Keywords
Related Articles
- EP ID EP748115
- DOI 10.21276/ijircst.2019.7.3.6
- Views 31
- Downloads 0
Blockchain Voting System- An NFT-Based Approach
This study introduces an innovative blockchain-based voting system that leverages non-fungible tokens to enhance the integrity, openness, and accessibility of elections. By harnessing the decentralized nature of blockcha...
Virtual Reallity in Gaming
Augmented reality (VR) gaming is another sort of PC game which utilizes computer generated reality (VR) innovations that can give clients a totally practical, first-individual viewpoint of the game activity. Designing mu...
Loan Eligibility Prediction Using Machine Learning
Banks and other financial institutions compete for customers by providing a wide range of services and products. Most banks, however, make the vast majority of their money from their credit portfolio. Loans accepted by b...
Hybrid Segmentation Algorithm using M-F based Optimization Model and Modified Fuzzy Clustering
In this paper, we propose and present an algorithm for medical image segmentation (MIS). By analyzing the current state-of-the-art related algorithms, we introduce the multi-band active contour model based limit function...
Enhancing Liver Segmentation: A Deep Learning Approach with EAS Feature Extraction and Multi-Scale Fusion
Deep learning technology have been broadly used in segmentation tasks of liver. To address the limitation of suboptimal segmentation for small targets, an end-to-end EAS(ECA-Attention and Separable convolution) U-Net is...