A fuzzy based classification scheme for unconstrained handwritten Devanagari character recognition

Shelke, Sushama; Apte, Shaila Dinkar

doi:10.1109/iccict.2015.7045738

Cited by 22 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection of restricted handwritten Devanagari characters has been studied. The categorization process used by the system was multi-stage [4 ] . Using multiple stage feature taking away, the detection is conceded out.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Deep Learning CNN Model for Recognition of Devanagari Digit

Bhosle¹,

Musande

2023

AIA

View full text Add to dashboard Cite

Devanagari character and digit recognition is a difficult undertaking because writing style depends on a person's traits and differs from person to person. We get more precise results in digit recognition thanks to deep learning convolutional neural networks (CNN), which function similarly to the human brain. In this study, the CNN method was put into practise and contrasted with the feed-forward neural network and random forest approaches. In comparison to previous methods, CNN has reportedly provided an accuracy rating of up to 99.2%. CNN is effective with both organised and unstructured data, including pictures, video, and audio.

show abstract

Section: Related Workmentioning

confidence: 99%

Evaluation of Deep Learning CNN Model for Recognition of Devanagari Digit

Bhosle¹,

Musande

2023

AIA

View full text Add to dashboard Cite

show abstract

“…According to the results of the experiments, the fuzzyvalued function had the highest character recognition rate of 87.7%. Shelke and Apte [4] introduced a model based on fuzzy based multi-stage classification for the recognition of handwritten devanagari script. This model classified 24 classes using fuzzy inference system.…”

Section: IImentioning

confidence: 99%

Recognition of Handwritten Currency Symbols using Fuzzy Inference System

Gupta¹

2021

IJRASET

View full text Add to dashboard Cite

In recent years, soft computing techniques have played an important role in the field of Handwritten Recognition. These techniques have been applied by different researchers to recognize handwritten scripts, alphabets, characters and so on. But no system has been proposed for the recognition of handwritten currency symbols. In this study, an automated intelligent system has been presented to recognize the handwritten currency symbols efficiently using Fuzzy Inference System (FIS). The experimental study has been conducted on the real dataset of the handwritten currency symbols of five topmost Asian richest countries. FIS has been implemented with subtractive clustering (SC) and fuzzy c-means (FCM) methods. The experimental result predicted that FIS with Subtractive Clustering achieved accuracy of 96.4% whereas FCM based FIS predicted 84% accuracy; hence SC-FIS outperformed FCM-FIS.

show abstract

“…[6] Implemented a system for recognition of Devanagari handwritten recognition using genetic algorithm based on diagonal features they have reported 85.78 % accuracy. [18] Proposed a multistage classification for recognition of unconstrained handwritten characters, first stage based on fuzzy inference system and second stage based on structural features using feed forward neural network and obtained 96.95% accuracy. [19] Implemented multistage neural network for recognition of numerals based on structural and geometric features and obtained detection rate of 93.17%.…”

Section: Literature Reviewmentioning

confidence: 99%

Handwritten Devanagari Numeral Recognition by Fusion of Classifiers

Prabhanjan¹,

Dinesh²

2015

IJSIP

View full text Add to dashboard Cite

The abstract is to Recognition of handwritten Devanagari numerals has many applications especially in the field of postal automation, document processing and so on. Due to its vast applications, many researchers are actively working towards development of effective and efficient hand written character/numeral recognition. Devanagari script is widely used script in Indian sub-continent, also devanagari script forms the basis for many other scripts in Indian sub-continent. In this paper, we have proposed a hybrid method to recognize handwritten devanagari numerals. The proposed method uses, stacking approach to fuse the confidence scores from four different classifiers viz., Naïve Bayes (NB), Instance Based Learner (IBK), Random Forest (RF), Sequential Minimal Optimization (SMO). Also, the proposed method extracts both local and global features from the handwritten numerals. In this work, we have used Fourier Descriptors as global shape feature. Whereas, the pixel density statistics from different zones of the numeral to describe the numerals locally. The proposed method has been tested on large set of handwritten numeral database and experimental results reveal that the proposed method yields the accuracy of 99.685%, which is the best accuracy reported so far for the datasets considered. Hence the proposed method outperforms contemporary algorithms.

show abstract

A fuzzy based classification scheme for unconstrained handwritten Devanagari character recognition

Cited by 22 publications

References 12 publications

Evaluation of Deep Learning CNN Model for Recognition of Devanagari Digit

Evaluation of Deep Learning CNN Model for Recognition of Devanagari Digit

Recognition of Handwritten Currency Symbols using Fuzzy Inference System

Handwritten Devanagari Numeral Recognition by Fusion of Classifiers

Contact Info

Product

Resources

About