%0 Journal Article %1 noauthororeditor %A Rao, N. Venkateswara %A Babu, Dr. B. Raveendra %D 2016 %J International Journal on Foundations of Computer Science & Technology (IJFCST) %K Components Connected Extraction Feature Handwritten Labeling Multi-Layer Networks Neural Perceptron Segmentation recognition %N 1 %P 17 %R 10.5121/ijfcst.2016.6104 %T SEGMENTATION AND RECOGNITION OF HANDWRITTEN DIGIT NUMERAL STRING USING A MULTI LAYER PERCEPTRON NEURAL NETWORKS %U https://wireilla.com/papers/ijfcst/V6N1/6116ijfcst04.pdf %V 6 %X In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also presents a new technique to remove slope and slant from handwritten numeral string and to normalize the size of text images and classify with supervised learning methods. Experimental results on a database of 102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained on independent digits contained in the numeral string of digits includes both the skewed and slant data.

@article{noauthororeditor, abstract = {In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also presents a new technique to remove slope and slant from handwritten numeral string and to normalize the size of text images and classify with supervised learning methods. Experimental results on a database of 102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained on independent digits contained in the numeral string of digits includes both the skewed and slant data. }, added-at = {2023-01-18T14:14:38.000+0100}, author = {Rao, N. Venkateswara and Babu, Dr. B. Raveendra}, biburl = {https://www.bibsonomy.org/bibtex/27a4573f7af6ea44e9c2a2a515fd59840/devino}, doi = {10.5121/ijfcst.2016.6104}, interhash = {16a333d44cf2c4a26ab78c0d79c2cab7}, intrahash = {7a4573f7af6ea44e9c2a2a515fd59840}, issn = {1839-7662}, journal = {International Journal on Foundations of Computer Science & Technology (IJFCST)}, keywords = {Components Connected Extraction Feature Handwritten Labeling Multi-Layer Networks Neural Perceptron Segmentation recognition}, month = jan, number = 1, pages = 17, timestamp = {2023-01-18T14:14:38.000+0100}, title = {SEGMENTATION AND RECOGNITION OF HANDWRITTEN DIGIT NUMERAL STRING USING A MULTI LAYER PERCEPTRON NEURAL NETWORKS}, url = {https://wireilla.com/papers/ijfcst/V6N1/6116ijfcst04.pdf}, volume = 6, year = 2016 }