activity
20102020
most citedHandwritten Bangla Basic and Compound character recognition using MLP and SVM classifier

81 citations · 532 across the 33 of their papers we have counts for

collaborators
Showing 2010 · cs.CVShow all

12 papers · 2 filters

cs.CV2010★ 12 cited

Recognition of handwritten Roman Numerals using Tesseract open source OCR engine

Sandip Rakshit, Amitava Kundu, Mrinmoy Maity +3

The objective of the paper is to recognize handwritten samples of Roman numerals using Tesseract open source Optical Character Recognition (OCR) engine. Tesseract is trained with d…

cs.CV2010★ 5 cited

Development of a Multi-User Recognition Engine for Handwritten Bangla Basic Characters and Digits

Sandip Rakshit, Debkumar Ghosal, Tanmoy Das +2

The objective of the paper is to recognize handwritten samples of basic Bangla characters using Tesseract open source Optical Character Recognition (OCR) engine under Apache Licens…

cs.CV2010★ 26 cited

Recognition of Handwritten Textual Annotations using Tesseract Open Source OCR Engine for information Just In Time (iJIT)

Sandip Rakshit, Subhadip Basu, Hisashi Ikeda

Objective of the current work is to develop an Optical Character Recognition (OCR) engine for information Just In Time (iJIT) system that can be used for recognition of handwritten…

cs.CV2010★ 4 cited

Recognition of Handwritten Roman Script Using Tesseract Open source OCR Engine

Sandip Rakshit, Subhadip Basu

In the present work, we have used Tesseract 2.01 open source Optical Character Recognition (OCR) Engine under Apache License 2.0 for recognition of handwriting samples of lower cas…

cs.CV2010★ 4 cited

Development of a multi-user handwriting recognition system using Tesseract open source OCR engine

Sandip Rakshit, Subhadip Basu

The objective of the paper is to recognize handwritten samples of lower case Roman script using Tesseract open source Optical Character Recognition (OCR) engine under Apache Licens…

cs.CV2010★ 4 cited

A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron

Nibaran Das, Ayatullah Faruk Mollah, Ram Sarkar +1

The work presents a comparative assessment of seven different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron (MLP) based classifier. The…