A method to reduce errors of string recognition based on combination of several recognition results with per-character alternatives

We consider the problem on recognition of a string object presented in several video stream frames. In order to maximize the output accuracy, we combine several results of the recognition. To this end, we consider a model of result of a string object recognition. The model takes into account the estimations of alternative results of per-character classification. Also, we propose an algorithm to combine results of a string recognition according to this model. The algorithm was evaluated on a MIDV-500 dataset of document images. The experimental results show that the proposed algorithm allows to achieve the high accuracy of recognition result due to an analysis of several images, and the use of the estimations of alternative results of per-character classification gives the higher results then a combination of strings that contain only the final alternatives of each character.