Neural Network for the Recognition of Handwritten Tunisian City Names
I. Ben Cheikh, Afef Kacem Echi · Proceedings of the International Conference on Document Analysis and Recognition · 2007
The complexity of the Arabic characters morphology makes research in recognition of the handwritten Arabic writing remain an interesting topic. In this setting, a system for recognition of handwritten Arabic words based on a Transparent Neural Network, called TNN-DF is developed within the LSTS laboratory. It uses structural features to describe words and makes recourse to Fourier descriptors (DF) when encounters an ambiguity. To enhance recognition results of TNN-DF, we suggest a neural approach to learn letters, part ofarabic words and words. Experiments conducted on 750 samples, of 50 city names, extracted from the standard IFN/ENIT' database of handwritten Tunisian city names show an improvement of recognition accuracy. The results are promising, and suggestions for improvements leading to recognition of larger voca bulary are proposed.