Feature extraction and analysis of handwritten words in grey-scale images using Gabor filters

We present a new off-line word recognition system that is able to recognise unconstrained handwritten words from their grey-scale images, and is based on structural and relational information in the handwritten word. We use Gabor filters to extract features from the words, and then use an evidence-based approach for word classification. A solution to the Gabor filter parameter estimation problem is given, enabling the Gabor filter to be automatically tuned to the word image properties. Our experiments show that the proposed method achieves reasonably high recognition rates compared to standard classification methods