Network Approach based Hindi Numeral Recognition

Handwriting has kept on persevering as a methods for correspondence and recording data in everyday life even with the presentation of new advancements. The steady improvement of PC apparatuses prompt the necessity of less demanding interface between the man and the PC. Written by hand character acknowledgment may for example be connected to Postal division acknowledgment, programmed printed frame securing, or checks perusing. The significance to these applications has prompted extraordinary research for quite a while in the field of disconnected manually written character acknowledgment. 'Hindi' the national dialect of India (written in Devanagri content) is world's third most prevalent dialect after Chinese and English. Hindi manually written character acknowledgment has got parcel of utilization in various fields like postal address perusing, checks perusing electronically. Acknowledgment of written by hand Hindi characters by PC machine is convoluted errand when contrasted with composed characters, which can be effortlessly perceived by the PC. This paper exhibits a plan to perceive hindi number numeral with the assistance of neural network

Feature Extraction Techniques for Marathi Character Classification using Neural Networks Models

Hand written Marathi Character Recognition is challenges to the researchers due to the complex structure. This paper presents a novel approach for recognition of unconstrained handwritten Marathi characters. The recognition is carried out using multiple feature extraction methods and classification scheme. The initial stages of feature extraction are based upon the pixel value features and the classification of the characters is done according to the structural parameters into 44 classes. The final stage of feature extraction makes use of the zoning features. First Pixel values are used as features and these values are further modified as another set of features. All these features are then applied to neural network for recognition. A separate neural network is built for each type of feature. The average recognition rate is found to be 67.96% , 82.67%,63,46% and 76.46% respectively for feed forward , radial basis , elman and pattern recognition neural networks for handwritten marathi characters