Title :
Simultaneous segmentation and recognition of Farsi/Latin printed texts with MLP
Author :
Menhaj, Mohammad B. ; Adab, Mahdi
Author_Institution :
Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran
fDate :
6/24/1905 12:00:00 AM
Abstract :
A new segmentation and recognition method for multi-font, multi-size Farsi/Arabic text using Multilayer feedforward neural networks is presented. The automatic feedback between segmentation and recognition completely removes or at least remarkably reduces the amount of errors due to segmentation. Furthermore, the common failures in recognizing Farsi/Arabic printed characters and in detecting some special lines are reduced. The OCR recognition method uses MLP type neural networks with Fourier descriptors
Keywords :
Fourier transforms; feedforward neural nets; image recognition; image segmentation; multilayer perceptrons; optical character recognition; Farsi/Arabic text; Farsi/Latin printed texts; Fourier descriptors; MLP type neural networks; OCR recognition method; multilayer feedforward neural networks; simultaneous segmentation and recognition; Character recognition; Feedforward neural networks; Multi-layer neural network; Natural languages; Neural networks; Optical character recognition software; Optical feedback; Shape; Speech recognition; Text recognition;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007745
