One way to solve this would be using a string similarity measures like Jaro-Winkler or the Levenshtein distance measure. However for a computer these are completely different making spotting these nearly identical strings difficult. The following table gives an example: Company Nameįor the human reader it is obvious that both Mc Donalds and Mac Donald’s are the same company. A similar problem occurs when you want to merge or join databases using the names as identifier. This is a problem, and you want to de-duplicate these. Databases often have multiple entries that relate to the same entity, for example a person or company, where one entry has a slightly different spelling then the other. It automatically traces high quality vector files from images and supports tracing of not only color and grayscale images, but also black and white as well.Ī problem that I have witnessed working with databases, and I think many other people with me, is name matching.
Super Vectorizer is a professional vector trace tool that enables the conversion from a raster bitmap images like JPEG, BMP and PNG to a scalable vector graphic with a few simple clicks. Super Vectorizer 2 - Vector Trace Tool for PC and Mac Screenshots.
Super Vectorizer 2 is a professional vector tracing software that automatically converts bitmap images like JPEG, GIF and PNG to clean, scalable vector graphic of Ai, SVG, DXF and PDF. Update: run all code in the below post with one line using string_grouper: Name Matching Using this approach made it possible to search for near duplicates in a set of 663,000 company names in 42 minutes using only a dual-core laptop. Using TF-IDF with N-Grams as terms to find similar strings transforms the problem into a matrix multiplication problem, which is computationally much cheaper. Traditional approaches to string matching such as the Jaro-Winkler or Levenshtein distance measure are too slow for large datasets.