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
