Al evaluar las dos soluciones, los revisores encontraron que scikit-image es más fácil de usar, configurar y hacer negocios en general. Sin embargo, prefirieron la facilidad de administración de OpenCV.
It is open source.That is the biggest plus point in contrast to Matlab which also has great image processing functions. I use it all the time especially when I need to to rapid prototyping with python as openCV supports python
For images where lighting condition are not very good it may not work perfectly. POC for Text recognition ,result use to vary as per the picture quality and lighting condition.
Me gusta la implementación fluida de esta biblioteca y métodos, y es bastante fácil integrarlos en tu código. Se puede combinar con la visualización utilizando la biblioteca matplotlib en Python, lo cual es bastante genial.
While it is very extensive, the library does have its limits with some data sets where the data will not be processed. Sometimes there is error when running it in MATLAB so that should be improved.
It is open source.That is the biggest plus point in contrast to Matlab which also has great image processing functions. I use it all the time especially when I need to to rapid prototyping with python as openCV supports python
Me gusta la implementación fluida de esta biblioteca y métodos, y es bastante fácil integrarlos en tu código. Se puede combinar con la visualización utilizando la biblioteca matplotlib en Python, lo cual es bastante genial.
For images where lighting condition are not very good it may not work perfectly. POC for Text recognition ,result use to vary as per the picture quality and lighting condition.
While it is very extensive, the library does have its limits with some data sets where the data will not be processed. Sometimes there is error when running it in MATLAB so that should be improved.