scholarly journals Online Handwritten Gurmukhi Character Recognition using Hybrid Feature Set

2018 ◽  
Vol 7 (3.4) ◽  
pp. 90 ◽  
Author(s):  
Mandeep Singh ◽  
Karun Verma ◽  
Bob Gill ◽  
Ramandeep Kaur
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Classification Models ◽  
Stroke Classification ◽  
Online Recognition ◽  
The World ◽  
Gurmukhi Script ◽  
Online Handwriting ◽  
Natural Way

Online handwriting character recognition is gaining attention from the researchers across the world because with the advent of touch based devices, a more natural way of communication is being explored. Stroke based online recognition system is proposed in this paper for a very complex Gurmukhi script. In this effort, recognition for 35 basic characters of Gurmukhi script has been implemented on the dataset of 2019 Gurmukhi samples. For this purpose, 32 stroke classes have been considered. Three types of features have been extracted. Hybrid of these features has been proposed in this paper to train the classification models. For stroke classification, three different classifiers namely, KNN, MLP and SVM are used and compared to evaluate the effectiveness of these models. A very promising “stroke recognition rate” of 94% by KNN, 95.04% by MLP and 95.04% by SVM has been obtained.  

ONLINE HANDWRITING CHARACTER RECOGNITION METHOD USING DIRECTIONAL AND DIRECTION-CHANGE FEATURES

1999 ◽  
Vol 13 (07) ◽  
pp. 1041-1059 ◽  
Author(s):  
MASAYOSHI OKAMOTO ◽  
KAZUHIKO YAMAMOTO
Keyword(s):  
Character Recognition ◽  
Traditional Method ◽  
Recognition Rate ◽  
Recognition Method ◽  
Online Recognition ◽  
Direction Change ◽  
Online Handwriting

We propose a new online recognition method to recognize handwritten cursive-style Japanese characters correctly. Our method simultaneously uses both directional features, otherwise known as offline features, and direction-change features which we designed as online features. The direction-change features express where in the mesh and in which direction the character's coordinates change. These features express both written strokes in the pen-down state and unwritten imaginary strokes in the pen-up state. The recognition rate was improved by our method over the traditional method using only directional features.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

A Prototype for Brazilian Bankcheck Recognition

1997 ◽  
Vol 11 (04) ◽  
pp. 549-569 ◽  
Author(s):  
Luan L. Lee ◽  
Miguel G. Lizarraga ◽  
Natanael R. Gomes ◽  
Alessandro L. Koerich
Keyword(s):  
Information Extraction ◽  
Character Recognition ◽  
Type I Error ◽  
Recognition Rate ◽  
Simulated Data ◽  
Recognition System ◽  
Signature Verification ◽  
Type I ◽  
Verification Algorithm ◽  
Commercial Applications

This paper describes a prototype for Brazilian bankcheck recognition. The description is divided into three topics: bankcheck information extraction, digit amount recognition and signature verification. In bankcheck information extraction, our algorithms provide signature and digit amount images free of background patterns and bankcheck printed information. In digit amount recognition, we dealt with the digit amount segmentation and implementation of a complete numeral character recognition system involving image processing, feature extraction and neural classification. In signature verification, we designed and implemented a static signature verification system suitable for banking and commercial applications. Our signature verification algorithm is capable of detecting both simple, random and skilled forgeries. The proposed automatic bankcheck recognition prototype was intensively tested by real bankcheck data as well as simulated data providing the following performance results: for skilled forgeries, 4.7% equal error rate; for random forgeries, zero Type I error and 7.3% Type II error; for bankcheck numerals, 92.7% correct recognition rate.

scholarly journals Handwritten tifinagh character recognition using simple geometric shapes and graphs

2019 ◽  
Vol 13 (2) ◽  
pp. 598
Author(s):  
Youssef Ouadid ◽  
Abderrahmane Elbalaoui ◽  
Mehdi Boutaounte ◽  
Mohamed Fakir ◽  
Brahim Minaoui
Keyword(s):  
Character Recognition ◽  
Graph Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Features Extraction ◽  
Experimental Results ◽  
Feature Points ◽  
Matching Method ◽  
Extraction Algorithm ◽  
Novel Algorithm

<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>

Probabilistic Model Based Hybrid Classifier for Character Recognition

2017 ◽  
Vol 25 (04) ◽  
pp. 621-647
Author(s):  
A. K. Sampath ◽  
N. Gomathi
Keyword(s):  
Probabilistic Model ◽  
Character Recognition ◽  
Recognition Rate ◽  
Size Variation ◽  
Orientation Angle ◽  
Recognition System ◽  
Input Image ◽  
Hybrid Classifier ◽  
Form Processing ◽  
Model Based

Handwritten character recognition is most crucial one indulging in many of the applications like forensic search, searching historical manuscripts, mail sorting, bank check reading, tax form processing, book and handwritten notes transcription etc. The problem occurrence in the recognition is mainly because of the writing style variation, size variation (length and height), orientation angle etc. In this paper a probabilistic model based hybrid classifier is proposed for the character recognition combining the neural network and decision tree classifiers. In addition to the local gradient features i.e. histogram oriented feature and grid level feature, an additional feature called GLCM feature is extracted from the input image in the proposed recognition system and are concatenated for the image recognition procedure to encode color, shape, texture, local as well as the statistical information. These extracted features considered are given to the hybrid classifier which recognises the character. In the test set, recognition accuracy of 95% is achieved. The proposed probabilistic model based hybrid classifier tends to contribute more accurate character recognition rate compared to the existing character recognition system.

scholarly journals APPLICATION OF ZONAL AND CURVATURE FEATURES TO NUMERALS RECOGNITION

2021 ◽  
Vol 9 (2) ◽  
pp. 7-12
Author(s):  
Binod Kumar Prasad
Keyword(s):  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Signature Verification ◽  
Optical Character ◽  
Knn Classifier ◽  
Average Recognition Rate ◽  
Distance Coding

Purpose of the study: The purpose of this work is to present an offline Optical Character Recognition system to recognise handwritten English numerals to help automation of document reading. It helps to avoid tedious and time-consuming manual typing to key in important information in a computer system to preserve it for a longer time. Methodology: This work applies Curvature Features of English numeral images by encoding them in terms of distance and slope. The finer local details of images have been extracted by using Zonal features. The feature vectors obtained from the combination of these features have been fed to the KNN classifier. The whole work has been executed using the MatLab Image Processing toolbox. Main Findings: The system produces an average recognition rate of 96.67% with K=1 whereas, with K=3, the rate increased to 97% with corresponding errors of 3.33% and 3% respectively. Out of all the ten numerals, some numerals like ‘3’ and ‘8’ have shown respectively lower recognition rates. It is because of the similarity between their structures. Applications of this study: The proposed work is related to the recognition of English numerals. The model can be used widely for recognition of any pattern like signature verification, face recognition, character or word recognition in another language under Natural Language Processing, etc. Novelty/Originality of this study: The novelty of the work lies in the process of feature extraction. Curves present in the structure of a numeral sample have been encoded based on distance and slope thereby presenting Distance features and Slope features. Vertical Delta Distance Coding (VDDC) and Horizontal Delta Distance Coding (HDDC) encode a curve from vertical and horizontal directions to reveal concavity and convexity from different angles.

scholarly journals Automatic Telugu Handwritten Character Recognition with the Help of Legendre-Sobolev Divergent Machine Learning Model

2019 ◽  
Vol 8 (2) ◽  
pp. 2283-2288
Keyword(s):  
Machine Learning ◽  
Character Recognition ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Machine Design ◽  
Wide Scope ◽  
Indian Language ◽  
Machine Learning Model ◽  
Online Handwriting Recognition ◽  
Online Handwriting

Online handwriting recognition or character recognition is the process in which a handwritten message is recognized by processing the handwritten data. It is the way toward changing over manually written characters to machine design. In penmanship, the strokes are made out of two arrange follows in the middle of pen down and pen up marks. Wide scope of highlights is extricated to play out thse acknowledgment. A complete internet hand-written recognition system for Indian language such as Telugu that addresses the ambiguities in separation just as recognition of buttons the recognition relies on conceptual model of penmanship structure joined with either a prejudicial model for stroke command. Such a methodology be able to flawlessly incorporate language and content data in the reproductive model then manage comparative and non-comparable strokes utilizing the single discriminative stroke grouping model. In this examination, we are utilizing disparate Legendre Sobolev conditions with the assistance of AI model, to such an extent that accomplishes 99.65% precision and improved the condition of craftsmanship esteem.

Offline Handwritten Gurumukhi Character Recognition System Using Convolution Neural Network

2019 ◽  
Vol 16 (10) ◽  
pp. 4164-4169
Author(s):  
Sheifali Gupta ◽  
Udit Jindal ◽  
Deepali Gupta ◽  
Rupesh Gupta
Keyword(s):  
Character Recognition ◽  
Recognition Accuracy ◽  
Recognition Task ◽  
Recognition System ◽  
Spoken Language ◽  
Processing Method ◽  
Specific Language ◽  
Novel Approach ◽  
The World ◽  
The Right

A lot of literature is available on the recognition of handwriting on scripts other than Indians, but the number of articles related to Indian scripts recognition such as Gurumukhi are much less. Gurumukhi is a religion-specific language that ranks 14th frequently spoken language in all languages of the world. In Gurumukhi script, some characters are alike to each other which makes recognition task very difficult. Therefore this article presents a novel approach for Gurumukhi character. This article lays emphasis on convolutional neural networks (CNN), which intend to obtain the features of given data samples and then its mapping is being performed to the right observation. In this approach, a dataset has been prepared for 10 Gurumukhi characters. The proposed methodology obtains a recognition accuracy of 99.34% on Gurumukhi characters images without making use of any post-processing method.

AN EFFICIENT POST PROCESSING ALGORITHM FOR ONLINE HANDWRITING GURMUKHI CHARACTER RECOGNITION USING SET THEORY

2013 ◽  
Vol 27 (04) ◽  
pp. 1353002 ◽  
Author(s):  
RAVINDER KUMAR ◽  
RAJENDRA KUMAR SHARMA
Keyword(s):  
Real Time ◽  
Set Theory ◽  
Efficient Algorithm ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Extensive Study ◽  
Post Processing ◽  
Single Character ◽  
Gurmukhi Script ◽  
Online Handwriting

In this paper, a post processor for accuracy of character recognition of real-time online Gurmukhi script has been developed. Our analysis is based on dataset consisting of 184 samples of each 45 characters of Gurmukhi script collected from four different categories of writers. Based on this extensive study, we propose an efficient algorithm for online handwritten Gurmukhi character recognition that achieves promising recognition accuracy of 95.6% for single character stroke sequencing. Beside character recognition the contribution in this paper is summarized in two folds as (i) the proposed scheme resolves stroke sequencing, (ii) overwritten strokes are identified and resolved. Moreover, for every stroke, complexity of adding new stroke for Gurmukhi character formation has been computed to be O(n).

A Reliability Design Methodology for Chinese Character Recognition

1998 ◽  
Vol 12 (02) ◽  
pp. 159-172 ◽  
Author(s):  
Y. S. Huang ◽  
K. Liu ◽  
C. Y. Suen ◽  
Y. Y. Tang
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition System ◽  
Reliability Design ◽  
Chinese Character Recognition ◽  
Nearest Neighbor Rule ◽  
Pattern Distribution

This paper proposes a novel method which enables a Chinese character recognition system to obtain reliable recognition. In this method, two thresholds, i.e. class region thresholdRk and disambiguity thresholdAk, are used by each Chinese character k when the classifier is designed based on the nearest neighbor rule, where Rk defines the pattern distribution region of character k, and Ak prevents the samples not belonging to character k from being ambiguously recognized as character k. A novel algorithm to derive the appropriate thresholds Ak and Rk is developed so that a better recognition reliability can be obtained through iterative learning. Experiments performed on the ITRI printed Chinese character database have achieved highly reliable recognition performance (such as 0.999 reliability with a 95.14% recognition rate), which shows the feasibility and effectiveness of the proposed method.

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