Data categorization for a context return applied to logical document structure recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Segmentation and Pre-Recognition of Arabic Handwriting
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Arabic word recognition by classifiers and context
Journal of Computer Science and Technology
WAV'09 Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science
A hybrid method for three segmentation level of handwritten Arabic script
Proceedings of the International Workshop on Multilingual OCR
Classifiers combination and syntax analysis for Arabic literal amount recognition
Engineering Applications of Artificial Intelligence
Human reading based strategies for off-line Arabic word recognition
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Hi-index | 0.00 |
We propose in this paper a recognition system of Arabic hand-written words issued from literal amounts of Arabic checks. This system is based on the idea of the PERCEPTRO system developed by M. Côté for Latin word recognition. It is a specific NN, named TransparentNeural Network (TNN), combining a global and a local vision modeling (GVM - LVM) of the word. In the forward propagation movement, the former (GVM) proposes a list of structural features characterizing the presence of some letters in the word. GVM proposes a list of possible letters and words containing these characteristics. Then, in the back-propagation movement, these letters are confirmed or not according to their proximity with corresponding printed letters. The correspondence between the letter shapes and the corresponding printed letters is performed by LVM using the correspondence of their Fourier descriptors (FD), playing the role of a letter shape normalizer.