Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Optimized handwritten character recognition using artificial neural network Subject The topic of the resource Artificial Neural Network; Binarization; Character recognition; Image processing; Kurtosis; Pattern recognition; Segmentation; Skeletonization; Skewness Description An account of the resource Handwritten character recognition (HCR) plays important role in the modern world and is one of the focused area of research in the field of image processing and pattern recognition. Handwritten character recognition refers to the process of conversion of hand-written character into printed/word file character which can immensely improve the interface between man and machine in numerous application. It is difficult to process with great variations in writing styles, different size and orientation angle of the character that are existing. Also segmentation of cursive handwritten text is difficult as the edges cant be detected easily. There are numerous approaches to recognize handwritten data. The images are acquired using a digital camera or scanner and stored in standard format like JPG, PNG etc. The second stages include pre-processing techniques like Binarization, Skeletonization, thinning, resizing the image and segmentation. In our work we mainly concentrated on extracting statistical features of alphabets like mean, variance, standard deviation, Skewness and kurtosis, which differentiates a character from another. We used feed forward algorithm to train Artificial Neural Network (ANN). The features of input character after pre-processing are fed into ANN. A database of 650 samples is created to test input samples for recognition of character by neural net-work. The Experimental results that we have achieved show 88.46 % accuracy rate with minimum time taken for training. IJSTR 2020. Creator An entity primarily responsible for making the resource Varma M.P.; Hore S.J.; Uday C.; Omnath Reddy S.; Pillai V.J. Source A related resource from which the described resource is derived International Journal of Scientific and Technology Research, Vol-9, No. 1, pp. 4442-4445. Publisher An entity responsible for making the resource available International Journal of Scientific and Technology Research Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081207796&amp;partnerID=40&amp;md5=87678d8ea8fe275241e7c00936b5a2b9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081207796&amp;partnerID=40&amp;md5=87678d8ea8fe275241e7c00936b5a2b9</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 22778616 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Varma M.P.; Hore S.J.; Uday C.; Omnath Reddy S.; Pillai V.J., CHRIST (Deemed to be University), Bangalore, India