In this paper, we develop an HMM-based sliding video text recognizer and present our results on Turkish broadcast news for the hearing impaired. We use well known speech recognition techniques to model and recognize sliding video text characters using a minimal amount of labeled data. Baseline system without any language modeling gives a word error rate of 2.2% on 138 minutes of test data. We then provide an analysis of character errors and employ a character-based language model to correct most of them. Finally we decrease the amount of training data to a quarter, split the test data into halves and investigate semi-supervised training. Word error rates after semi-supervised training are significantly lower than to those after baseline training. We see 40% relative reduction in word error rate (1.5 → 0.9) over the test set.
['Tk Som', 'Dogan Can', 'Murat Saraclar']
HMM-based sliding video text recognition for Turkish broadcast news