The aim of the article is to assess the effectiveness of the use of Gabor filters in the identification and classification of images depicting handwriting, in particular in the context of recognition of letters, numbers or whole words. The article presents an optoelectronic method of identifying the characteristics of handwriting images based on the Gabor wavelet transform. This method has not been published anywhere before, but it opens up new perspectives for the study of handwriting. The research problem was formulated: Does the use of Gabor filters allow for effective feature extraction from handwriting images, enabling accurate identification and classification of characters compared to other feature extraction methods? Accordingly to the research problem posed, a research hypothesis was formulated, which assumes that Gabor filters, thanks to their ability to detect local patterns in images (such as edges, textures and directional structures), are an effective method of extracting features in tasks related to the identification of handwriting images, surpassing other commonly used methods in terms of classification accuracy. In the article, the authors proposed a simple algorithm for recognizing images in the diffraction space for forensic purposes. The question remains, can this method be used in practice? In order to obtain an answer to the research problem and verify the research hypothesis, research methods such as analysis of both domestic and foreign literature, comparative analysis, as well as mathematical modeling were used.
