R.B. Azimov
Approaches to the recognition of handwritten letters of the Azerbaijani language and their analysis
Optical character recognition (OCR) computer systems are widely used for recognition of car license plates, for enabling visually impaired people to work with written information, etc. One of the current areas of OCR problems is the recognition of handwritten characters. This study proposes approaches to the construction of combined models using simple and ensemble learning for recognition of handwritten letters of the Azerbaijani language. The models built using the proposed approaches utilized several classes of features. Based on the results of computer experiments, a comparative analysis of the recognition accuracy of the constructed models and features using the proposed recognition methods is conducted.
Keywords: OCR, Machine learning, Ensemble learning, ANN, CNN, K-means
DOI: https://doi.org/10.54381/icp.2023.1.05
Approaches to the recognition of handwritten letters of the Azerbaijani language and their analysis
Optical character recognition (OCR) computer systems are widely used for recognition of car license plates, for enabling visually impaired people to work with written information, etc. One of the current areas of OCR problems is the recognition of handwritten characters. This study proposes approaches to the construction of combined models using simple and ensemble learning for recognition of handwritten letters of the Azerbaijani language. The models built using the proposed approaches utilized several classes of features. Based on the results of computer experiments, a comparative analysis of the recognition accuracy of the constructed models and features using the proposed recognition methods is conducted.
Keywords: OCR, Machine learning, Ensemble learning, ANN, CNN, K-means
DOI: https://doi.org/10.54381/icp.2023.1.05