Cracks in oil paintings constitute an undesirable but unavoidable effect of time, deteriorating the painting quality. This work proposes a crack detection method that supports the physical restoration process of the artworks, providing a fissure map that allows the artist to visualize the pictorial layer and its flaws. This approach applies three image processing techniques to digitized oil paintings: oriented elongated filters, top-hat morphological filters and a K-SVD algorithm. Then, a post-processing stage based on K-Means is performed on the resulting binary maps to eliminate false positives. Finally, a pixel-by-pixel voting technique is applied to combine the binary maps. Our proposed framework has a better performance detecting craquelure when compared to other methods such as ADA Boost and convolutional neural networks. We obtained a recall of 0.8577, a probability of false alarm of 0.0779, a probability of false negatives of 0.1423, an accuracy of 0.7123, and an F1 value of 0.7783, which is amongst the best results for the state-of-the-art techniques.