Initially OCRopus was actually using Tesseract as recognition engine inside, but later they changed it to their own brand-new engine. It is still fresh and not mature. We have been making accuracy comparison about year ago, and OCRopus was definitely losing to Tesseract, I am not even talking about commercial enignes. Since then I stopped … Read more
Although you already choose BRISK, you might find FREAK interesting. Author claims to have better results than BRISK and ORB. I should also add that ORB is scale-invariant but has some problems in that area. So I would still recommend it for someone to try it. The FREAK source code is compatible with OpenCV (how … Read more
We used deep convolutional networks on spectrograms for a spoken language identification task. We had around 95% accuracy on a dataset provided in this TopCoder contest. The details are here. Plain convolutional networks do not capture the temporal characteristics, so for example in this work the output of the convolutional network was fed to a … Read more
If you read the docs for OneHotEncoder you’ll see the input for fit is “Input array of type int”. So you need to do two steps for your one hot encoded data from sklearn import preprocessing cat_features = [‘color’, ‘director_name’, ‘actor_2_name’] enc = preprocessing.LabelEncoder() enc.fit(cat_features) new_cat_features = enc.transform(cat_features) print new_cat_features # [1 2 0] new_cat_features … Read more
You can use hog class in opencv as follows HOGDescriptor hog; vector<float> ders; vector<Point> locs; This function computes the hog features for you hog.compute(grayImg, ders, Size(32, 32), Size(0, 0), locs); The HOG features computed for grayImg are stored in ders vector to make it into a matrix, which can be used later for training. Mat … Read more
Note that the results might not necessarily be so different. You might simply need different hyperparameters for the two options to give similar results. The ideal thing is to test what works best for your problem. If you can’t afford this for some reason, most algorithms will probably benefit from standardization more so than from … Read more
A feature detector is an algorithm which takes an image and outputs locations (i.e. pixel coordinates) of significant areas in your image. An example of this is a corner detector, which outputs the locations of corners in your image but does not tell you any other information about the features detected. A feature descriptor is … Read more
This is probably a bit late to the table, but… As Bee points out and you are already aware, the use of SVM as a classifier is wasted if you have already lost the information in the stages prior to classification. However, the process of text classification requires much more that just a couple of … Read more
Feature detection In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the … Read more
This doesn’t require loops. # Create and fit selector selector = SelectKBest(f_classif, k=5) selector.fit(features_df, target) # Get columns to keep and create new dataframe with those only cols_idxs = selector.get_support(indices=True) features_df_new = features_df.iloc[:,cols_idxs]