The preceding algorithm in code is performed as follows (the code file is available as Handwritten_text_recognition.ipynb in GitHub):
- Download and import the dataset. This dataset will contain the images of handwritten text and their corresponding ground truth (transcription).
- Build a function that resizes pictures without distorting the aspect ratio and pad the rest of pictures so that all of them have the same shape:
def extract_img(img): target = np.ones((32,128))*255 new_shape1 = 32/img.shape[0] new_shape2 = 128/img.shape[1] final_shape = min(new_shape1, new_shape2) new_x = int(img.shape[0]*final_shape) new_y = int(img.shape[1]*final_shape) img2 = cv2.resize(img, (new_y,new_x )) target[:new_x,:new_y] = img2[:,:,0] target[new_x:,new_y:]=255 ...
