Text recognition using neural networks
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Date
2023
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Abstract
Text recognition is one of the key tasks in the field of deep learning. In recent years, neural networks and deep learning have attracted increasing attention in the context of developing effective and accurate models for text recognition. This scientific dissertation explores the use of neural networks and the Python programming language to solve the problem of text recognition. First, an overview of existing methods and approaches to text recognition, including classical rule-based methods and traditional machine learning, is presented. The following is an implementation of text recognition using the TensorFlow library and the Python programming language. The principles of data preprocessing, including tokenization, vectorization and normalization, are described, Experiments are being conducted on various data sets, including handwritten text and text on images. Various hyperparameters of the model are investigated, such as the network architecture, the size of the hidden layer and the learning rate. Thus, this scientific dissertation confirms the importance and effectiveness of using neural networks and the Python programming language for the task of text recognition. This opens up prospects for further research and development in the field of text recognition and other natural language processing tasks.
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text recognition, learning, networks, the Python programming language