A Novel Method for Recognition of Persian Alphabet by Using Fuzzy Neural Network

Mohammad Mehdi Motahari Kia; Jafar A. Alzubi; Mehdi Gheisari; Xiaobo Zhang; Mohamadtaghi Rahimi; Yongrui Qin

doi:10.1109/ACCESS.2018.2881050

A Novel Method for Recognition of Persian Alphabet by Using Fuzzy Neural Network

Mohammad Mehdi Motahari Kia, Jafar A. Alzubi, Mehdi Gheisari, Xiaobo Zhang, Mohamadtaghi Rahimi, Yongrui Qin

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

Abstract

This paper presents a system that can recognize handwritten words expressed using broken letters of the Persian alphabet. The proposed system can be used for most activities related to the gathering of public information. Statistical features of the separated/broken letters are employed in the system. Each letter is recognized using interconnected fuzzy neural network. The advantages of this method include high precision owing to the strength of the neural network algorithm and the possibility of extending dataset instance codes in a simple manner. At last, an evaluation for the proposed method is provided experimentally.

Original language	English
Article number	8532339
Pages (from-to)	77265-77271
Number of pages	7
Journal	IEEE Access
Volume	6
DOIs	https://doi.org/10.1109/ACCESS.2018.2881050
Publication status	Published - 12 Nov 2018

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SDG 9 Industry, Innovation, and Infrastructure

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title = "A Novel Method for Recognition of Persian Alphabet by Using Fuzzy Neural Network",

abstract = "This paper presents a system that can recognize handwritten words expressed using broken letters of the Persian alphabet. The proposed system can be used for most activities related to the gathering of public information. Statistical features of the separated/broken letters are employed in the system. Each letter is recognized using interconnected fuzzy neural network. The advantages of this method include high precision owing to the strength of the neural network algorithm and the possibility of extending dataset instance codes in a simple manner. At last, an evaluation for the proposed method is provided experimentally.",

keywords = "letter partitioning, method for searching context line, Persian alphabet recognition, smoothing",

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year = "2018",

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AU - Mehdi Motahari Kia, Mohammad

AU - Alzubi, Jafar A.

AU - Gheisari, Mehdi

AU - Zhang, Xiaobo

AU - Rahimi, Mohamadtaghi

AU - Qin, Yongrui

PY - 2018/11/12

Y1 - 2018/11/12

N2 - This paper presents a system that can recognize handwritten words expressed using broken letters of the Persian alphabet. The proposed system can be used for most activities related to the gathering of public information. Statistical features of the separated/broken letters are employed in the system. Each letter is recognized using interconnected fuzzy neural network. The advantages of this method include high precision owing to the strength of the neural network algorithm and the possibility of extending dataset instance codes in a simple manner. At last, an evaluation for the proposed method is provided experimentally.

AB - This paper presents a system that can recognize handwritten words expressed using broken letters of the Persian alphabet. The proposed system can be used for most activities related to the gathering of public information. Statistical features of the separated/broken letters are employed in the system. Each letter is recognized using interconnected fuzzy neural network. The advantages of this method include high precision owing to the strength of the neural network algorithm and the possibility of extending dataset instance codes in a simple manner. At last, an evaluation for the proposed method is provided experimentally.

KW - letter partitioning

KW - method for searching context line

KW - Persian alphabet recognition

KW - smoothing

UR - https://www.scopus.com/pages/publications/85056573821

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DO - 10.1109/ACCESS.2018.2881050

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SP - 77265

EP - 77271

JO - IEEE Access

JF - IEEE Access

M1 - 8532339

ER -

A Novel Method for Recognition of Persian Alphabet by Using Fuzzy Neural Network

Abstract

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