ما هي الخوارزميات العصبية المستخدمة في تحسين التعرف على النصوص؟
Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Connectionist Temporal Classification (CTC), Transformer-based Models (BERT and GPT)
Various sets of algorithms or rather architectures can be leveraged to make text recognition algorithms more efficient. Each with a varying level of effectiveness and compatibility with system for a given problem.
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There are several neural algorithms commonly used to improve text recognition in various applications. Here are a few notable ones:
1. Convolutional Neural Networks (CNNs): Widely used for text recognition tasks, such as optical character recognition (OCR). These networks utilize convolutional layers to capture local features and patterns in the input text images.
2. Recurrent Neural Networks (RNNs): An another popular choice for text recognition, particularly for tasks involving sequential data, such as handwriting recognition. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are commonly used RNN variants that can capture contextual information and dependencies among characters or words in a sequence.
(FYI) Apart from these, we have something called Data Augmentation, Although not a specific neural algorithm, Data Augmentation techniques play a crucial role in improving text recognition. Techniques such as rotation, scaling, translation, and adding noise to text images can help increase the robustness of the models by introducing variations in the training data.
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