@article{Ankur Garg ,Payal Deora ,D.Malathi_2020, title={Handwritten Text to Editable Text Document}, volume={29}, url={http://sersc.org/journals/index.php/IJAST/article/view/28474}, abstractNote={<p><em>On</em><em>e of the major problem faced by every organization is management of old records of handwritten text that &nbsp;are likely to be subjected &nbsp;to further deterioration in the future. These old records are difficult to manage &nbsp;because of the sheer volume they exist in. This issue can be rectified if there existed a softcopy of those records but the Analysis of Handwritten text has been one of the major challenge in the field of image processing because of various writing styles, background lighting and text orientation. All the existing technique &nbsp;have &nbsp;failed when the letters in a text cannot &nbsp;be segmented &nbsp;properly. In &nbsp;our project, the &nbsp;text &nbsp;will be segmented&nbsp; &nbsp;and&nbsp; &nbsp;then&nbsp;&nbsp; segmented&nbsp; &nbsp;letters &nbsp;will &nbsp;be &nbsp;analysed&nbsp; &nbsp;and classified into appropriate class using a neural network .With the aid of Natural Language&nbsp; processing, we can analyse the context of a misclassified word and predict its occurrence in reference to the context.&nbsp; &nbsp;With&nbsp; &nbsp;a &nbsp;proper &nbsp;blend&nbsp; &nbsp;of &nbsp;image&nbsp;&nbsp; processing,&nbsp; &nbsp;Natural language &nbsp;processing and Convolutional Neural Network, we will recognize the text with a high degree of accuracy.</em></p&gt;}, number={2}, journal={International Journal of Advanced Science and Technology}, author={Ankur Garg ,Payal Deora ,D.Malathi}, year={2020}, month={Jul.}, pages={4707 - 4712} }