Multi-Digit Handwritten Sindhi Numerals Recognition using SOM Neural Network

Chandio, Asghar Ali; Jalbani, Akhtar Hussian; Laghari, Mehwish; Awan, Shafique Ahmed

doi:10.22581/muet1982.1704.14

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Also, the system may be designed to detect the movement of the pen tip on the screen. In other words, handwriting recognition may involve a system detecting movements of a pen tip on the screen to get a clue of the characters being written [7]. Handwriting recognition can be classified into two: offline recognition and online recognition.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Handwriting Recognition Using Artificial Intelligence Neural Network and Image Processing

2023

IRJMETS

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Due to increased usage of digital technologies in all sectors and in almost all day to day activities to store and pass information, Handwriting character recognition has become a popular subject of research. Handwriting remains relevant, but people still want to have Handwriting copies converted into electronic copies that can be communicated and stored electronically. Handwriting character recognition refers to the computer's ability to detect and interpret intelligible Handwriting input from Handwriting sources such as touch screens, photographs, paper documents, and other sources. Handwriting characters remain complex since different individuals have different handwriting styles. This paper aims to report the development of a Handwriting character recognition system that will be used to read students and lectures Handwriting notes. The development is based on an artificial neural network, which is a field of study in artificial intelligence. Different techniques and methods are used to develop a Handwriting character recognition system. However, few of them focus on neural networks. The use of neural networks for recognizing Handwriting characters is more efficient and robust compared with other computing techniques. The paper also outlines the methodology, design, and architecture of the Handwriting character recognition system and testing and results of the system development. The aim is to demonstrate the effectiveness of neural networks for Handwriting character recognition.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Handwriting Recognition Using Artificial Intelligence Neural Network and Image Processing

2023

IRJMETS

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“…Authors in a study [11] worked on multi-digit handwritten Sindhi Numerals using SOM (Self-Organizing map). This system is able to recognize any style and different font size numerals.…”

Section: Related Workmentioning

confidence: 99%

Off-line Sindhi Handwritten Character Identification

Kumari¹,

Sangrasi²,

Bhatti³

et al. 2019

IJITCS

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Handwritten Identification is an ability of the computer to receive and translate the intelligible handwritten text into machine-editable text. It is classified into two types based on the way input is given namely: off-line and online. In Off-line handwritten recognition, the input is given in the form of the image while in online input is entered on a touch screen device. The research on off-line and online handwritten Sindhi character identification is on its very initial stage in comparison to other languages. Sindhi is one of the subcontinent's oldest languages with extensive literature and rich culture. Therefore, this paper aims to identify off-line Sindhi handwritten characters. In the proposed work, major steps involve in characters identification are training and testing of the system. Training is performed using a feed-forward neural network based on the efficient accelerative technique, the Back Propagation (BP) learning algorithm with momentum term and adaptive learning rate. The dataset of 304 Sindhi handwritten characters is collected from 16 different Sindhi writers, each with 19 characters. The novelty of proposed work is the comparison of the recognition rate for the single character, two characters and three characters at a time. Results showed that the recognition rate achieved for a single character is more than the recognition rate of multiple characters at a time.

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“…This method involves obtaining digital data from a static representation of handwriting, where the system reads and converts the handwriting document into a digital format. On the other hand, real time recognition involves detecting or converting characters as they are being written on a specialized screen [6,7]. The system identifies characters *Corresponding author: maryam84@coeduw.uobaghdad.edu.iq https://wjcm.uowasit.edu.iq/index.php/wjcm and words by detecting the movement of the pen tip.…”

Section: Introductionmentioning

confidence: 99%

“…The human visual system plays a crucial role in reading handwriting characters, words, letters, and digits, but this process is not as simple as it may seem. Although the brain unconsciously processes information based on prior learning, people may not realize the complexity of recognizing handwriting [6,9]. Creating a computer system that can accomplish this task is a daunting challenge.…”

Section: Introductionmentioning

confidence: 99%

Real time handwriting recognition system using CNN algorithms

Maryam Yaseen Abdullah

2023

WJCMS

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Abstract— The growing use of digital technologies across various sectors and daily activities has made handwriting recognition a popular research topic. Despite the continued relevance of handwriting, people still require the conversion of handwritten copies into digital versions that can be stored and shared digitally. Handwriting recognition involves the computer's strength to identify and understand legible handwriting input data from various sources, including document, photo-graphs and others. Handwriting recognition pose a complexity challenge due to the diversity in handwriting styles among different individuals especially in real time applications. In this paper, an automatic system was designed to handwriting recognition using the recent artificial intelligent algorithms, the conventional neural network (CNN). Different CNN models were tested and modified to produce a system has two important features high performance accuracy and less testing time. These features are the most important factors for real time applications. The experimental results were conducted on a dataset includes over 400,000 handwritten names; the best performance accuracy results were 99.8% for SqueezeNet model.

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Multi-Digit Handwritten Sindhi Numerals Recognition using SOM Neural Network

Cited by 5 publications

References 6 publications

Handwriting Recognition Using Artificial Intelligence Neural Network and Image Processing

Handwriting Recognition Using Artificial Intelligence Neural Network and Image Processing

Off-line Sindhi Handwritten Character Identification

Real time handwriting recognition system using CNN algorithms

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