Learning Multi-level Dependencies for Robust Word Recognition
Table 3: Performance comparison different type of noise in terms of accuracy (%). Best results are highlighted with bold numbers.
['Method', 'W-PER', 'W-DEL', 'W-INS', 'W-SUB']
Next, we take one step further by removing the constraint that the noise will not affect the first and last characters of each word. More specifically, we define 4 new types of noise that are W-PER, W-DEL, W-INS, and W-SUB, which stand for altering a word by permuting the whole word, deleting, inserting, and substituting characters in any position of the word. Similarly, for each type of new noise, we construct a noised dataset. 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.