A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification
Table 5: The comparison of our system to the state-of-the-art systems on the RIMES dataset in terms of number of recognizers (nrec), homogeneity of the algorithm (Hom.), complexity of the approach (Compl.), and word accuracy (%) (wordacc). Please, refer to the text for further explanation.
['system', 'n [ITALIC] rec', 'Hom.', 'Compl.', 'word [ITALIC] acc']
; Ptucha et al. in the terms of a number of characteristics: number of recognizers, homogeneity of the algorithm, word accuracy (%) and the complexity of the approach, not to be confused with computational complexity, e.g. deep learning method without extra complicated modules. The results indicate that it is possible to achieve a high word accuracy (%) in comparison to the state of the art with a limited-size ensemble, a homogeneous algorithmic approach and a low complexity Stuner et al. Poznanski and Wolf ; Ptucha et al. (cf. In those studies, numerous networks (up to 118 or 2100 network instances) are required in the ensemble. Whereas our method only uses five networks, yielding comparable or better results. In the proposed method, feature descriptors such as histogram of oriented gradients (HOG Dalal and Triggs