Offline Handwriting Recognition to Determine the Handwriting Accents – A Pilot Study

2020 ◽  
Author(s):  
Vinny Sharma ◽  
A.K. Jain ◽  
Prashant Johri ◽  
Gunjan Jindal
Keyword(s):  
Pilot Study ◽  
Handwriting Recognition ◽  
Offline Handwriting Recognition

A FULLY AUTOMATED OFFLINE HANDWRITING RECOGNITION SYSTEM INCORPORATING RULE BASED NEURAL NETWORK VALIDATED SEGMENTATION AND HYBRID NEURAL NETWORK CLASSIFIER

2004 ◽  
Vol 18 (07) ◽  
pp. 1267-1283 ◽  
Author(s):  
MOUMITA GHOSH ◽  
RANADHIR GHOSH ◽  
BRIJESH VERMA
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Solution Method ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Neural Network Classifier ◽  
Hybrid Neural Network ◽  
Rule Based ◽  
Extraction Algorithm ◽  
Offline Handwriting Recognition

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing.

Progress in the Raytheon BBN Arabic Offline Handwriting Recognition System

2014 ◽  
Author(s):  
Huaigu Cao ◽  
Prem Natarajan ◽  
Xujun Peng ◽  
Krishna Subramanian ◽  
David Belanger ◽  
...  
Keyword(s):  
Handwriting Recognition ◽  
Recognition System ◽  
Offline Handwriting Recognition
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