S
Satya Prakash Singh
Researcher at Birla Institute of Technology, Mesra
Publications - 33
Citations - 163
Satya Prakash Singh is an academic researcher from Birla Institute of Technology, Mesra. The author has contributed to research in topics: Document classification & Computer science. The author has an hindex of 5, co-authored 25 publications receiving 94 citations. Previous affiliations of Satya Prakash Singh include G H Patel College Of Engineering & Technology & Birla Institute of Technology and Science.
Papers
More filters
Journal ArticleDOI
An efficient Devanagari character classification in printed and handwritten documents using SVM
Shalini Puri,Satya Prakash Singh +1 more
TL;DR: An efficient Devanagari character classification model using SVM for printed and handwritten mono-lingual Hindi, Sanskrit and Marathi documents, which first preprocesses the image, segments it through projection profiles, removes shirorekha, extracts features, and then classifies the shirorikha-less characters into pre-defined character categories.
Proceedings ArticleDOI
A technical study and analysis of text classification techniques in N - Lingual documents
Shalini Puri,Satya Prakash Singh +1 more
TL;DR: A technical study and analysis is presented to show N-lingual document classification for normal text, printed and handwritten documents and three statistically analyzed charts are shown, which are based on content type classification, language-mode pair and most-to-least preferred languages of existing algorithms.
Journal ArticleDOI
Hindi Text Document Classification System Using SVM and Fuzzy: A Survey
Shalini Puri,Satya Prakash Singh +1 more
TL;DR: A new idea of Hindi printed and handwritten document classification system using support vector machine and fuzzy logic first pre-processes and then classifies textual imaged documents into predefined categories.
Book ChapterDOI
An Efficient Hindi Text Classification Model Using SVM
Shalini Puri,Satya Prakash Singh +1 more
TL;DR: A Hindi Text Classification model is proposed, which accepts a set of known Hindi documents, preprocesses them at document, sentence and word levels, extracts features, and trains SVM classifier, which further classifies aSet of Hindi unknown documents.
Proceedings ArticleDOI
Software effort estimation using Neuro-fuzzy approach
TL;DR: Comparative Analysis between Neuro-fuzzy model and the traditional software model(s) such as Halstead, WalstonFelix, Bailey-Basili and Doty models is provided and evaluation criteria are based upon MMRE (Mean Magnitude of Relative Error) and RMSE (Root mean Square Error).