Simplifying automatic recognition algorithms for hand-printed characters attracted immense research efforts [1-3]. Character recognition systems can improve the interaction between man and machine in many applications, including office automation, business and data entry applications. This paper introduces the use of bi-dimensional wavelet as features extractor that is feed to Artificial Neural Networks (ANNs) for recognition Latin hand-printed characters. An experiment to verify the efficiency of the system was performed. The proposed technique can be divided into three major steps: the first step is pre-processing in which the original image is transformed into a digitized image utilizing a 300 dpi scanner. Second, feature extraction using wavelets Finally, multilayer artificial neural network is used for characters recognition.
El-Nahry, I. (2021). Hand Printed Characters Recognition Using Wavelet Features and Neural Networks.. MEJ- Mansoura Engineering Journal, 28(4), 11-20. doi: 10.21608/bfemu.2021.142390
MLA
I. F. El-Nahry. "Hand Printed Characters Recognition Using Wavelet Features and Neural Networks.". MEJ- Mansoura Engineering Journal, 28, 4, 2021, 11-20. doi: 10.21608/bfemu.2021.142390
HARVARD
El-Nahry, I. (2021). 'Hand Printed Characters Recognition Using Wavelet Features and Neural Networks.', MEJ- Mansoura Engineering Journal, 28(4), pp. 11-20. doi: 10.21608/bfemu.2021.142390
VANCOUVER
El-Nahry, I. Hand Printed Characters Recognition Using Wavelet Features and Neural Networks.. MEJ- Mansoura Engineering Journal, 2021; 28(4): 11-20. doi: 10.21608/bfemu.2021.142390
)
View on SCiNiTO