ANALYZING THE INFLUENCE OF HYPERPARAMETERS ON THE EFFICIENCY OF OCR MODEL FOR PRE-REFORM HANDWRITTEN TEXTS

The article considers the influence of hyperparameters on the efficiency of models of optical handwriting recognition of pre-reform period on the example of handwritten reports of governors of the Yenisei province of the XIX century. A comparative analysis of model configurations with different architectural components, including normalization modules, feature extraction blocks and predictors, is carried out. Particular attention is paid to the role of input image resolution and the size of hidden layers in achieving an optimal balance between prediction accuracy and computational cost. The results obtained allow us to identify key parameters for the development of optical character recognition systems adapted to historical texts with non-standard orthography and complex structure. Prospects for further research include evaluating synthetic methods for extending training data and analyzing alternative architectures such as transformers.