Image Classification and Text Extraction using Convolutional Neural Network

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ID: 313290
2021
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Abstract
As luck would have its recent innovations in computer vision grant us to make considerable pace abate the data classification and analysis of enormous data in different organizations like training material, policy guides, and project documents that can be used internally. In addition, cloud service providers are rising in text detection techniques and offer many computer vision contributions included Google Vision, A WS Textract, and Azure OCR. In this paper image classification is achieved by CNN algorithm which is the top choice for image classification. Then using CNN, we extract text data from images. CNN has better performance rate on large datasets as it controls the problem of overfitting. Hence, the accuracy of these algorithms can be enhanced by increasing the epochs and integrating a large dataset. This approach used a methodology to resolve the number of convolution and pooling layers with the number of nodes in network. Lastly, CNN algorithms are better when data is appropriate if it has mortal features that can be analyzed and utilized by algorithm.
Reference Key
imported_1777060303_69ebc9cf3af71 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rabia Zaman, Rafia Bashir, Atif Raza Zaidi
Journal Journal of Computing & Biomedical Informatics
Year 2021
DOI
10.56979/201/2021/23
URL
https://jcbi.org/index.php/Main/article/view/23
Keywords Keywords not found

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