from normalize import Normalizer from recognize import LetterRecognizer from model import LetterClassifier # creating the recognizer # it uses `tesserocr` to provide us with very rough letter boxes # because those boxes are pretty rough, we have to normalize them first # by rough prediction of letter box I mean a box with 2 or more letters in it(or maybe none at all) rec = LetterRecognizer(image_path='./res/table_real.jpg') letters_raw = rec.find_letters() # creating normalizer(cuts all the letters into the small images and pads them to the same size) normalizer = Normalizer(letter_sequence=letters_raw) letters_norm = normalizer.normalized_letters() # creating SGD classifier model classifier = LetterClassifier(gamma=1e-4) classifier.load(dump_filename='model.dmp') # resulting in labels labels = classifier.predict(letters_norm)
