Assistive Technologies for Visual, Hearing, and Speech Impairments: Machine Learning and Deep Learning Solutions

Shahira, K. C.; Sruthi, C. J.; Lijiya, A

doi:10.1002/9781119821908.ch17

Cited by 4 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional machine learning methods, such as support vector machines (SVMs), k-nearest neighbor (KNN), and decision trees, have been widely used in different applications from medical decision making to smart city [9][10][11][12]. In own case, document categorization methods typically rely on feature extraction techniques, such as term frequency-inverse document frequency (TF-IDF) and latent semantic analysis (LSA), to extract relevant features from the text [13][14][15]. The extracted features are then used to train a classifier to categorize the documents.…”

Section: Related Workmentioning

confidence: 99%

Automatic Classification of Scanned Electronic University Documents using Deep Neural Networks with Conv2D Layers

Baimakhanova,

Zhumadillayeva,

Avdarsol

et al. 2023

IJACSA

View full text Add to dashboard Cite

This paper proposes a novel approach for scanned document categorization using a deep neural network architecture. The proposed approach leverages the strengths of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract features from the scanned documents and model the dependencies between words in the documents. The pre-processed documents are first fed into a CNN, which learns and extracts features from the images. The extracted features are then passed through an RNN, which models the sequential nature of the text. The RNN produces a probability distribution over the predefined categories, and the document is classified into the category with the highest probability. The proposed approach is evaluated on a dataset of scanned documents, where each document is categorized into one of four predefined categories. The experimental results demonstrate that the proposed approach achieves high accuracy and outperforms existing methods. The proposed approach achieves an overall accuracy of 97.3%, which is significantly higher than the existing methods' accuracy. Additionally, the proposed approach's performance was robust to variations in the quality of the scanned documents and the OCR accuracy. The contributions of this paper are twofold. Firstly, it proposes a novel approach for scanned document categorization using deep neural networks that leverages the strengths of CNNs and RNNs. Secondly; it demonstrates the effectiveness of the proposed approach on a dataset of scanned documents, highlighting its potential applications in various domains, such as information retrieval, data mining, and document management. The proposed approach can help organizations manage and analyze large volumes of data efficiently.

show abstract