Learning Multi-level Dependencies for Robust Word Recognition
Table 2: Performance comparison with different types of noise in terms of accuracy (%). Best results are highlighted with bold numbers.
['Method', 'INT', 'DEL', 'INS', 'SUB']
There are several observations can be made from the table. The first is that model based methods (scRNN and MUDE) achieve much better performance than dictionary based one (Enchant). This is not surprising as model based methods can effectively utilize the sequential dependencies of words in the sentences. Moreover, MUDE consistently outperforms scRNN in all cases, which we believe attributes to the effective design of MUDE to capture both character and word level dependencies. More detailed analysis of contribution brought by the character-level dependencies will be shown later in this section. In addition, we observe that the difficulty brought by different types of noise varies significantly. Generally, for model based methods, permutation and insertion noises are relatively easier to deal with comparing to deletion and substitution noises. We argue this is because the former ones do not lose any character information. In other words, the original character information is largely preserved with permutation and insertion. On the contrary, both deletion and substitution can cause information loss, which makes it harder to recognize the original words. This again demonstrate how important the character-level information is. Finally, the results also show that in more difficult situations where deletion or substitution noises present, the advantages of the MUDE become even more obvious. This clearly suggests the effectiveness of the MUDE. This suggests the new types of noise are more difficult to handle, which is expected as they cause more variations of noised words. More importantly, as the difficulty of the task increases, the advantages of MUDE over scRNN also become more obvious. Such observation is also consistent with previous findings. which can not utilize the character-level dependencies. Thus, in the next subsection, we conduct analysis on the contribution of character-level dependencies to gain better understanding of MUDE.