Multi-Resolution Pre-Processing for Pattern Recognition in Images and Audio Signals

With the rapid growth of technology and the proliferation of data in this digital age, current image and audio applications require greater resolution, higher data transmission rates and better data compression techniques to meet the ever increasing demands placed on them. The research presented here investigates the impact of data compression in the automatic recognition of handwritten digit images and spoken digit audio. A Haar wavelet transform (HWT) is used to compress the original image and audio data, which is input to an artificial neural network (ANN) where the automatic digit recognition is performed. The HWT generates a signature, or fingerprint, for the data by removing redundant data using a cut-off function, a number of which are investigated. This reduced data signature enables the ANN-based recogniser to be simplified and computationally more efficient. Experimental results show that for handwritten digit images, the recognition accuracy is 94.3% with compression ratios of 80%; for spoken audio digits, the recognition accuracy is 98.8% with compression ratios of 82%.