Fuzzy Base Artificial Neural Network Model for Text Extraction From Images

Narayana, V. Lakshman; Sudheer, B. Naga; Maddumala, Venkata Rao; Papasani, Anusha

doi:10.31838/jcr.07.06.61

Cited by 6 publications

(3 citation statements)

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“…There is no perfect one stop solution for recognizing the script of all the languages using a single method. The deep learning and machine learning approaches can be useful for achieving this task.Another approach of using Fuzzy rule-based decision and artificial neural network to detect the characters and numbers has presented in [5]. The system is designed which works for detecting characters and numbers present in the images by using pure image processing techniques the system is designed to detect the handwritten textual part of the images.…”

Section: Literature Reviewmentioning

confidence: 99%

Text Similarity From Image Contentsusing Statistical and Semantic Analysis Techniques

Kulkarni,

Govilkar,

Amin

2023

Artificial Intelligence, NLP , Data Science and Cloud Computing Technology

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Plagiarism detection is one of the most researched areas among the Natural Language Processing(NLP) community. A good plagiarism detection covers all the NLP methods including semantics, named entities, paraphrases etc. and produces detailed plagiarism reports. Detection of Cross Lingual Plagiarism requires deep knowledge of various advanced methods and algorithms to perform effective text similarity checking. Nowadays the plagiarists are also advancing themselves from hiding the identity from being catch in such offense. The plagiarists are bypassed from being detected with techniques like paraphrasing, synonym replacement, mismatching citations, translating one language to another. Image Content Plagiarism Detection (ICPD) has gained importance, utilizing advanced image content processing to identify instances of plagiarism to ensure the integrity of image content. The issue of plagiarism extends beyond textual content, as images such as figures, graphs, and tables also have the potential to be plagiarized. However, image content plagiarism detection remains an unaddressed challenge. Therefore, there is a critical need to develop methods and systems for detecting plagiarism in image content. In this paper, the system has been implemented to detect plagiarism form contents of Images such as Figures, Graphs, Tables etc. Along with statistical algorithms such as Jaccard and Cosine, introducing semantic algorithms such as LSA, BERT, WordNet outperformed in detecting efficient and accurate plagiarism.

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Section: Literature Reviewmentioning

confidence: 99%

Text Similarity From Image Contentsusing Statistical and Semantic Analysis Techniques

Kulkarni,

Govilkar,

Amin

2023

Artificial Intelligence, NLP , Data Science and Cloud Computing Technology

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“…The algorithm used for this system should be able to recognize each human action which needs to be predefined before training. Thus, compared with unsupervised learning, supervised learning which can use labeled training set for classification tasks is the optimal solution for designing such a recognition system [25]. According to the literature study, some supervised learning methods are selected for our classifier.…”

Section: A Machine Learning Modelsmentioning

confidence: 99%

An Efficient Classifier using Machine Learning Technique for Individual Action Identification

Sravanthi¹,

Devi²,

Sandeep³

et al. 2020

IJACSA

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Human action recognition is an important branch of computer vision and is getting increasing attention from researchers. It has been applied in many areas including surveillance, healthcare, sports and computer games. This proposed work focuses on designing a human action recognition system for a human interaction dataset. Literature research is conducted to determine suitable algorithms for action recognition. In this proposed work, three machine learning models are implemented as the classifiers for human actions. An image processing method and a projection-based feature extraction algorithm are presented to generate training examples for the classifier. The action recognition task is divided into two parts: 4-class human posture recognition and 5-class human motion recognition. Classifiers are trained to classify input data into one of the posture or motion classes. Performance evaluations of the classifiers are carried out to assess validation accuracy and test accuracy for action recognition. The architecture designs for the centralized and distributed recognition systems are presented. Later these designed architectures are simulated on the sensor network to evaluate feasibility and recognition performance. Overall, the designed classifiers show a promising performance for action recognition.

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“…One of the fundamental reasons that make the two people and organizations use cloud storage supplier is to consistently have a reinforcement of their significant information [18]. This reinforcement will consistently make the information accessible whenever with the goal that the clients can without much of a stretch access it in whatever time and spot they need [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Secure Data Uploading and Accessing Sensitive Data Using Time Level Locked Encryption to Provide an Efficient Cloud Framework

Narayana¹,

Gopi²,

Paturi³

et al. 2020

ISI

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Executing cloud computing participate from multiple points of view for Web-based overseeing commitments to mark various issues. Here the assurance and data security has considered into a significant issue with downside that limits numerous applications that identified with cloud. These days cloud computing has been generally perceived as one of the most compelling data advances in view of its phenomenal focal points. Regardless of its generally perceived social and monetary advantages, in cloud computing clients lose the immediate control of their information and totally depend on the cloud to deal with their information and calculation, which raises huge security and protection concerns and is one of the significant obstructions to the appropriation of open cloud by numerous associations and people. In the proposed work a Time Level Locked Encryption (TLLE) framework is designed for deduplication of huge documents to accomplish space-proficient capacity in cloud and also to monitor data uploading and data access. The proposed model is compared with the traditional AES encryption model and the results show that the proposed model is exhibiting better performance.

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Fuzzy Base Artificial Neural Network Model for Text Extraction From Images

Cited by 6 publications

References 0 publications

Text Similarity From Image Contentsusing Statistical and Semantic Analysis Techniques

Text Similarity From Image Contentsusing Statistical and Semantic Analysis Techniques

An Efficient Classifier using Machine Learning Technique for Individual Action Identification

Secure Data Uploading and Accessing Sensitive Data Using Time Level Locked Encryption to Provide an Efficient Cloud Framework

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