Braille translation based on multi-knowledge

The transformation from Braille to Mandarin Characters can be divided into two steps: from Braille to Pinyin and from Pinyin to Mandarin Characters. Incorporating the Legal Pinyin Table into our system the ambiguity problem was solved in the transformation from Braille to Pinyin. A standard statistical Bigram Markov model was used in the subsystem to transform Pinyin to Mandarin Characters. Then two modifications of the smoothing method which are consistent with the phrase-level Bigram model were proposed to overcome the sparse data problem in our system model. For each Pinyin sentence, a multi-level graph was used with the Viterbi algorithm to search for the best Mandarin sentence in the maximal likelihood. The measurement of N-best algorithm was studied to get N best Mandarin sentences. Experiments show that the correct rate of the system is 94. 38%. If proper nouns are not considered, our system can achieve a further 2% improvement. The accuracy rate for the top-5 hypothesis by using N-Best algorithm is 3% higher than that of the best hypothesis.