Analysis of Methods for Classification and Aggregation of Textual Data From Images

This study investigates modern methods of text recognition from images, specifically comparing optical character recognition and intelligent character recognition. The technologies of machine learning, including convolutional and recurrent neural networks, are compared based on criteria such as accuracy and efficiency in processing handwritten and printed texts. The advantages and limitations of existing solutions for forming digital documents from images containing various handwriting styles and low-quality text images are analyzed. Key challenges associated with processing multilingual texts are identified, and future prospects for the development of text recognition technologies are discussed.