An off-line cursive handwriting recognition system

1998 ◽  
Vol 20 (3) ◽  
pp. 309-321 ◽  
Author(s):  
A.W. Senior ◽  
A.J. Robinson
Keyword(s):  
Handwriting Recognition ◽  
Recognition System ◽  
Cursive Handwriting

USING A STATISTICAL LANGUAGE MODEL TO IMPROVE THE PERFORMANCE OF AN HMM-BASED CURSIVE HANDWRITING RECOGNITION SYSTEM

2001 ◽  
Vol 15 (01) ◽  
pp. 65-90 ◽  
Author(s):  
U.-V. MARTI ◽  
H. BUNKE
Keyword(s):  
Hidden Markov ◽  
Handwriting Recognition ◽  
Language Model ◽  
Recognition System ◽  
Difficult Problem ◽  
Language Models ◽  
Linguistic Knowledge ◽  
Statistical Language Model ◽  
Cursive Handwriting ◽  
Handwritten Text

In this paper, a system for the reading of totally unconstrained handwritten text is presented. The kernel of the system is a hidden Markov model (HMM) for handwriting recognition. This HMM is enhanced by a statistical language model. Thus linguistic knowledge beyond the lexicon level is incorporated in the recognition process. Another novel feature of the system is that the HMM is applied in such a way that the difficult problem of segmenting a line of text into individual words is avoided. A number of experiments with various language models and large vocabularies have been conducted. The language models used in the system were also analytically compared based on their perplexity.

scholarly journals Artificial Neural Network for Cursive Handwriting Recognition System

2020 ◽  
Vol 9 (5) ◽  
pp. 877-880
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Neural System ◽  
Cursive Handwriting ◽  
Testing Zone ◽  
Artificial Neural ◽  
The One

Cursive Handwriting acknowledgment is an extremely testing zone because of the one of a kind styles of composing starting with one individual then onto the next. Right now, disconnected cursive composing character acknowledgment framework is portrayed utilizing an Artificial Neural Network. The highlights of every character written in the information are extricated and afterward sent to the neural system. Informational collections, having writings of various individuals are utilized in making framework. The suggested acknowledgment framework yields elevated steps of exactness when contrasted with the ordinary methodologies right now. This framework can effectively perceive cursive messages and convert them into auxiliary structure.

OFFLINE HANDWRITING RECOGNITION USING SYNTHETIC TRAINING DATA PRODUCED BY MEANS OF A GEOMETRICAL DISTORTION MODEL

2004 ◽  
Vol 18 (07) ◽  
pp. 1285-1302 ◽  
Author(s):  
T. VARGA ◽  
H. BUNKE
Keyword(s):  
Recognition Performance ◽  
Handwriting Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Training Data ◽  
Training Set ◽  
Experimental Conditions ◽  
Perturbation Model ◽  
Cursive Handwriting ◽  
Synthetic Training Data

A perturbation model for the generation of synthetic textlines from existing cursively handwritten lines of text produced by human writers is presented. The goal of synthetic textline generation is to improve the performance of an offline cursive handwriting recognition system by providing it with additional training data. It can be expected that by adding synthetic training data the variability of the training set improves, which leads to a higher recognition rate. On the other hand, synthetic training data may bias a recognizer towards unnatural handwriting styles, which could lead to a deterioration of the recognition rate. In this paper the proposed perturbation model is evaluated under several experimental conditions, and it is shown that significant improvement of the recognition performance is possible even when the original training set is large and the textlines are provided by a large number of different writers.

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.

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