Word Level Multi-Script Identification Using Curvelet Transform in Log-Polar Domain

Sahare, Parul; Chaudhari, Ravindra; Dhok, Sanjay B.

doi:10.1080/03772063.2018.1430516

Cited by 10 publications

(4 citation statements)

References 42 publications

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“…It depends on the functions to be removed. The removed attributes must be able to classify the character habit [18,19]. In this application, the research work have provided a great advantage by using a horizontal cover there is also a maximum limit for functions.…”

Section: Methodsmentioning

confidence: 99%

Enhanced Handwritten Document Recognition Using Confusion Matrix Analysis

Umadevi¹,

Murugan²

2021

Advances in Parallel Computing Technologies and Applications

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The handwritten Multilanguage phase is the preprocessing phase that improves the image quality for better identification in the system. The main goals of preprocessing are diodes, noise suppression and line cancellation. After word processing, various attribute extraction techniques are used to process attribute properties for the identification process. Smoothing plays an important role in character recognition. The partitioning process in the word distribution strategy can be divided into global and local texts. The writer does not use this header line to write the text which creates a problem for skew correction, classification and recognition. The dataset used are HWSC and TST1. The tensor flow method is used to estimate the consistency of confusion matrix for the enhancement of the text recognition .The accuracy of the proposed method is 98%.

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Section: Methodsmentioning

confidence: 99%

Enhanced Handwritten Document Recognition Using Confusion Matrix Analysis

Umadevi¹,

Murugan²

2021

Advances in Parallel Computing Technologies and Applications

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“…e basic level is to combine data calculation and information storage so that machines can independently calculate and transmit information. e middle layer is where the machine can make decisions and actions based on judgments and decisions, and the last layer is where the machine automatically performs deep learning of algorithms and information sources to perform targeted actions [12]. At present, it has proven its capabilities in the military, medical, and Internet fields.…”

Section: Artificial Intelligence Technologymentioning

confidence: 99%

“…Artificial intelligence technologies that are currently being explored in greater depth include expert systems, machine learning, and neural networks [11]. e application of these new technologies in computer technology, on the one hand, can meet the needs of information system users to a certain extent; on the other hand, it will also contribute to the development of informatics [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Improved Dynamic PPI Network Construction and Application of Data Mining in Computer Artificial Intelligence Systems

Tang

2022

Scientific Programming

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The protein interaction network is a network composed of the interactions between all proteins in living organisms. The use of computational methods to prove the functional modules in the PPI network is an important research topic in bioinformatics. With the development of technologies for detecting protein interactions, the available data on interactions has become more and more extensive, and the PPI networks created have become more and more extensive. At the same time, with the deepening of research, people have found that the real PPI network is constantly changing with time and conditions and this change is closely related to the creation and development of life activities. Therefore, it is also very important and necessary to understand and study the functional modules of the PPI dynamic network. At the same time, it focuses on analyzing the basic attributes of artificial intelligence and computer technology and analyzing the goals that can be achieved by using artificial intelligence in computer technology. The effects and directions that can be further explored can be extracted from a large amount of unrealistic, incomplete, fuzzy, and random actual data. These data are hidden from people who do not know them before , but this is a potentially useful process for obtaining information and knowledge. Applying data mining technology to diagnose computer network problems can improve reliability, stability, flexibility, troubleshooting speed, and proper network management, automatically diagnose, predict, and maintain network failures, and ensure that the network has high-quality services and reliability. Drive AI applications in computer science.

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“…So, designing a handwritten numeral recognizer which can work in a multilingual environment will be of huge significance in the Indian context. A lot of research work (Chatterjee et al, 2019;Hangarge et al, 2013;Obaidullah et al, 2019;Pardeshi et al, 2014;Sahare et al, 2019;Singh et al, 2019;Singh, Sarkar, Bhateja, & Nasipuri, 2018;Singh, Sarkar, Nasipuri, & Doermann, 2015) has already been done for the recognition of characters (considering only text), but only a few work (Obaidullah et al, 2015;Obaidullah et al, 2016;Rakshit et al, 2019;Roy et al, 2004) has been extended to numerals. Motivated by the research gap, our approach aims to recognize handwritten numerals in four major scripts used in the Indian sub-continent.…”

Section: Existing Workmentioning

confidence: 99%

A new feature extraction approach for script invariant handwritten numeral recognition

et al. 2021

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Handwritten numeral recognition is a challenging research problem because of the enormous varieties of styles in which human beings write the numerals. Several researchers have tried to find solutions to this problem with exceptional recognition accuracies. However, most of these solutions have been dedicated to single script numerals. Such methods are inappropriate for multi-lingual nations such as India where a large number of scripts are used. Keeping this issue in mind, a new feature descriptor named symbolization of binary images (SBI) is introduced here for the recognition of handwritten numerals of different scripts. Effectiveness of SBI is supported with experiments showing its script-invariant nature. Classification of numerals using a multiclass support vector machine (SVM) classifier yields the recognition accuracies of 98.18, 96.22, 96.52, and 95.53% on datasets of numerals written in four popular scripts of the world: Arabic, Bangla, Devanagari, and Latin, respectively. This scheme has also been extended to the situation when the script used is not known a priori or the numerals written in a document belong to pairs of mixed scripts of {Arabic, Devanagari, Bangla} with Latin producing recognition rates of 92.97, 91.25, and 91.67%, respectively. When all four scripts are mixed, the recognition rate is still 90.98% overall. Encouraging outcomes suggest that the proposed SBI feature descriptor can recognize numerals invariant of the script class.

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Word Level Multi-Script Identification Using Curvelet Transform in Log-Polar Domain

Cited by 10 publications

References 42 publications

Enhanced Handwritten Document Recognition Using Confusion Matrix Analysis

Enhanced Handwritten Document Recognition Using Confusion Matrix Analysis

Improved Dynamic PPI Network Construction and Application of Data Mining in Computer Artificial Intelligence Systems

A new feature extraction approach for script invariant handwritten numeral recognition

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