A New Approach for Arabic Handwritten Postal Addresses Recognition

Charfi, Manel; Kherallah, Monji; Baati, Abdelkarim El; Alimi, Adel M.

doi:10.14569/ijacsa.2012.030301

Cited by 6 publications

(3 citation statements)

References 24 publications

(42 reference statements)

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“…Fusion of different statistical feature extraction techniques like: Run Length Matrix, Moment Invariants, Intensity Histogram, Gray Level Co-occurrence Matrix to develop a new hybrid method that improved the recognition accuracy for Arabic characters [21]. A hand written postal address recognition system based on elliptical beta was presented for Arabic language [22]. A standard deviation & and zone centric based feature extraction method for better accuracy while training and testing the Neural Network combined with Genetic Algorithm for recognition of Odia handwritten character was presented [23].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Recognition of Hand Written Devnagri Compound Characters

Yawalkar¹,

Kharat²

2019

AJCST

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Being an effective tool in the world of communication, numerous techniques have been developed for documenting the handwritten text. Few of the exceptional techniques describe the environment of handwritten scripts and further convert it into electronic data by implementing various algorithms. Devnagri is one of the widely used scripts for most popular and commonly used languages like Marathi and Hindi. Recent development in the field of handwritten character recognition based on different methodologies like neural network, fuzzy logic, and deep neural networks has shown remarkable improvement in character recognition accuracy from 75% to 96%. We propose a fuzzy-Neural hybrid approach for recognition of hand written Devnagri compound character that uses a rotation invariant rule-based thinning algorithm as one of the major pre-processing activity. Thinning the characters to their central line, preserving the shape of the character are the distinctive features of thinning algorithm. Concurrent application of different rules to each pixel of the character image results into symmetrical thinning as well as improves the overall speed of the system. The system is trained using Neural Network where the weights are optimized using fuzzy rules improving the accuracy of the system.Results obtained for the fuzzy-neural based system with thinning helps in preserving the topology of the characters written in Devnagri and prove that accuracy of the system has stabilized in the band of 92-97% which was fluctuating in the band of 89-94% for the previously implemented systems. The system also shows a substantial improvement in accuracy for recognition of compound characters in comparison with our previously implemented system.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Recognition of Hand Written Devnagri Compound Characters

Yawalkar¹,

Kharat²

2019

AJCST

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“…Postal address recognition system for Arabic language is proposed by M. Charfi et al (2012) Writing translates style of writing, Mood and personality of the writer, which makes it difficult to characterize. From scanned envelop, printed boarder and stamp logo are suppressed.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Ensemble Classifiers for Handwriting Recognition

Govindarajan¹

2013

IJMECS

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-One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed for homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of real and benchmark data sets of recognizing totally unconstrained handwritten numerals. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase and combining phase. A wide range of comparative experiments are conducted for real and benchmark data sets of recognizing totally unconstrained handwritten numerals. The accuracy of base classifiers is compared with homogeneous and heterogeneous models for data mining problem. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and also heterogeneous models exhibit better results than homogeneous models for real and benchmark data sets of recognizing totally unconstrained handwritten numerals.

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“…Offline Recognition is referred to as the ability of the computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs by optical scanning, also referred to as Optical Character Recognition or Intelligent word Recognition. Many researchers [1]- [6] have find the solution for the Offline character recognition with good recognition rates.…”

Section: Introductionmentioning

confidence: 99%

Online hand written character recognition using Digital Pen for static authentication

Renuka

Suganya

2014

2014 International Conference on Computer Communication and Informatics

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Due to the rapid development of computer technology, Human Computer Interaction (HCI) techniques have become an indispensable component in our daily life. HCI focuses on the understanding between humans and the computer. Also the protection of password involves different methods and it is being a great challenge to the computer industry. Hand Written Character is one form of human interaction can be used effectively in HCI for static authentication with better password protection. In this paper, the Digital Pen consists of triaxial micro-electromechanical (MEMS) Accelerometer, to capture the activities of the human and to capture motion trajection information from accelerations for recognizing gestures for handwriting. The pen operates in two modes. It can operate as mouse by recognizing the gestures and other mode it can operate as Handwritten Character Recognition for identifying the digits and characters also in this mode it provides static authentication for the systems by recognizing the characters. An Integrated Development Interface is developed using dotnet to recognize the characters.

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A New Approach for Arabic Handwritten Postal Addresses Recognition

Cited by 6 publications

References 24 publications

A Hybrid Approach for Recognition of Hand Written Devnagri Compound Characters

A Hybrid Approach for Recognition of Hand Written Devnagri Compound Characters

Evaluation of Ensemble Classifiers for Handwriting Recognition

Online hand written character recognition using Digital Pen for static authentication

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