What are the approaches to handwritten text recognition?

Handwritten Text Recognition (HTR) involves several main approaches:

1. Classical feature-based methods: extracting characteristic features (contours, angles, strokes) and using machine learning algorithms (SVM, HMM) for character classification.

2. Neural networks and deep learning:

   • Convolutional Neural Networks (CNNs) for feature extraction from images.
   • Recurrent Neural Networks (RNNs), LSTM or GRU for processing sequences of characters and considering context.
   • Combination of CNN + RNN with attention mechanisms to improve accuracy.

3. End-to-End models: modern architectures that directly convert handwritten text images into character strings without intermediate segmentation steps.

4. Use of transformers: attention-based models that are successfully applied to sequence and image processing.

Depending on the task and available data, the appropriate method is chosen. For example, optimized CNN models are often used in mobile applications, while hybrid architectures with RNN and attention are used for complex documents.