A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification
Table 6: The results of the KdK dataset. The Table shows the average (avg) and standard deviation (sd) of word accuracy (%) of five architectures using 5-fold cross-validation and varying per architecture, over the following parameters: dictionary (none, Concise, Large)and coding scheme (Plain, Extra separator) (5×3×2). Each row is derived from 30 network evaluations.
['Arch', 'avg', 'sd']
Each row is derived from 30 network evaluations. In other words, each row is the result of one architecture, regardless of the used CTC decoding method, dictionary, and coding scheme. Slightly lower performance is expected as the Best-path CTC decoder pulls it down. Similar result is achieved for each coding scheme, regardless of the used CTC decoding method, dictionary, and architecture. The Extra-separator has a higher performance, 94.5%, which is 0.4 pp higher than the Plain decoding scheme.