Sentence examples for digit database from inspiring English sources

Figure 6 Performance of WSCMN features on Marathi digit database in white noise environment.

This network performs a 10-class classification of the MNIST handwritten digit database [21] (Fig. 7).

The accuracy of all classes using SIFT are mostly higher when using MFCC except in the English digit database.

The Aurora 2.0 noisy digit database is widely used for the evaluation of noise-robust frontends [14].

Experimental results on the ORL and Yale face databases and the MNIST handwritten digit database show the robustness of the proposed methods.

The speech recognition experiments were conducted under clean and noisy conditions using the TI-46 and own created Marathi digit database.

In the first environment, the Aurora-2 connected US-digit database [37] is the platform for evaluating the proposed MSE and other various techniques.

In the experiments conducted on the Aurora-2 noisy-digit database, the presented WS-HEQ yields significant recognition improvements relative to the Mel-scaled filter-bank cepstral coefficient (MFCC) baseline and to cepstral histogram normalization (CHN) in various noise-corrupted situations and exhibits a behavior superior to that of S-HEQ in most cases.

The lowest different accuracy is in the English digits database.

The Isolated digits database (0 9, o) has 454 samples for each class.

WSCMN feature performance was also tested on clean as well as noisy Marathi digits database.