parser.add_argument('--experience_number', '-e', type=int, action='store', default=df.EXP_NUMBER, help="The number of the training experience.") parser.add_argument('--font-properties', '-f', type=str, action='store', default=df.FONT_PROPERTIES, help="The path of a file containing font properties for a list of training fonts.") parser.add_argument('--font-size', '-s', type=int, action='store', default=df.FONT_SIZE, help="The font size of the training font, in px.") parser.add_argument('--tessdata-path', '-p', type=str, action='store', default=df.TESSDATA_PATH, help="The path of the tessdata/ directory on your filesystem.") parser.add_argument('--word_list', '-w', type=str, action='store', default=df.WORD_LIST, help="The path of a file containing a list of frequent words.") parser.add_argument('--verbose', '-v', action='store_true', help="Use this argument if you want to display the training output.") args = parser.parse_args() perform_security_checks(args) # Check validity of args # Training process trainer = TesseractTrainer(dictionary_name=args.tesseract_lang, text=args.training_text, font_name=args.font_name, font_path=args.font_path, font_size=args.font_size, exp_number=args.experience_number, font_properties=args.font_properties, tessdata_path=args.tessdata_path, word_list=args.word_list, verbose=args.verbose) trainer.training() # generate a multipage tif from args.training_text, train on it and generate a traineddata file trainer.clean() # remove all files generated in the training process (except the traineddata file) trainer.add_trained_data() # copy the traineddata file to the tessdata/ directory