Deep learning based hand written character recognition for manuscript documents | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 1439-1447 اصل مقاله (1.23 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5790 | ||
| نویسندگان | ||
| T Jerry Alexander* 1؛ S Suresh Kumar2؛ N R Krishnamoorthy3 | ||
| 1Faculty of Electronics Engineering, Sathyabama Institute of Science & Technology, IT Highway, Chennai, India | ||
| 2Principal, Swarnandhra College of Engineering & Technology, Narasapur, India | ||
| 3School of Electrical & Electronics, Sathyabama Institute of Science & Technology, Chennai, India | ||
| چکیده | ||
| Handwritten manuscripts contain much ancient information related to astrology, medicines, grammar etc. They are of various forms such as palm leaves, paper, stones etc. These manuscripts are preserved by the method of digitization with noise introduced. By using proper filtering as well as denoising methods these noises are eliminated and the images are restored. It is finally required to recognize the handwritten characters automatically from the restored image enabling the researchers and enthusiasts for going through the document very easily. This proposed work deals with the creation of a handwritten characters dataset for all the characters within a specific dimensional area and the recognition of handwritten characters using the deep learning method. First, the handwritten dataset is created from different human handwritings in a specific format, scanned and each character with suitable dimension is obtained by labelling them as per the sequence. Then various forms of convolution network are applied for the character recognition and the results are compared to obtain the suitable net for the Tamil character recognition from the handwritten document. | ||
| کلیدواژهها | ||
| Character recognition؛ Convolution Neural Network؛ Historical Manuscripts | ||
| مراجع | ||
|
| ||
|
آمار تعداد مشاهده مقاله: 46,188 تعداد دریافت فایل اصل مقاله: 50,570 |
||
| تعداد نشریات | 22 |
| تعداد شمارهها | 722 |
| تعداد مقالات | 10,381 |
| تعداد مشاهده مقاله | 72,829,721 |
| تعداد دریافت فایل اصل مقاله | 64,523,901 |