In addition, our approach is easy to train and can be used as a drop-in complement for any text-spotting algorithm that outputs a ranking of word hypotheses.
in [431]: #plt.figure(figsize=(1,0.0008)) data = np.array ([[0.65966403,0.80212915],[0.3854018,0.25270265],[0.1903537,0.5453093]]) #labels = np.array ([['way','SWE'], ['DSWE','D'], ['DSWE','D'] figure(figsize=(3,2) figure(figsize=(3,2) figure(figsize=(3,2)