Sign Language Recognition Using Python and OpenCV - DataFlair

Now we calculate the threshold value for every frame and determine the contours using cv2.findContours and return the max contours (the most outermost contours for the object) using the function segment. Using the contours we are able to determine if there is any foreground object being detected in the ROI, in other words, if there is a hand in the ROI. When contours are detected (or hand is present in the ROI), We start to save the image of the ROI in the train and test set respectively for the letter or number we are detecting it for. In the above example, the dataset for 1 is being created and the thresholded image of the ROI is being shown in the next window and this frame of ROI is being saved in ..train/1/example.jpg For the train dataset, we save 701 images for each number to be detected, and for the test dataset, we do the same and create 40 images for each number. Now on the created data set we train a CNN. First, we load the data using ImageDataGenerator of keras through which we can use the flow_from_directory function to load the train and test set data, and each of the names of the number folders will be the class names for the imgs loaded.