Calculating the average distance between horizontal projection peaks

During the analysis and construction of the noise filtering algorithm at the stage of segmentation of character strings, the need to describe a special database or dictionary for storing model and skeletal projections of alphabet characters became clear. However, when formatting entries for alphabetical projection in the dictionary, the following questions arose: how many projection values are needed to describe a single character, and also how this value may change depending on the number of strings analyzed and their characters. The objects of research in the article are the vertical projection of the characters, as well as their width of the projection segments. The subject of research is the change of the average width of the projection segment for a certain type of license plates (character string). The main goal is to calculate and justify the average size of the segment. Data about the average width of the projection segment that describes a character allows to determine the number of projection values (coordinates along the ox axis) to store the projection values of this character from an alphabet. Several language alphabets are considered, where each of them is associated with a unique type. In particular, license plates of the Republic of Belarus and the Slovak Republic are considered. Using an elementary statistical apparatus, calculations and analysis of samples were carried out, taking into account the case of their high variation. As a result, the average values of the segment width and the variance of the segment width were obtained using two types of alphabets as an example. In research an algorithm for using the obtained values in the formation of model projection records is presented. The algorithm takes into account «special» cases of going beyond the segment boundaries. The described steps and calculations can be applied to a larger number of alphabets, which indicates the possibility of describing model projection dictionaries for them, with subsequent widespread use of character strings in noise filtering.

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