Bangla Character Recognition using Artificial Neural Network Step:Classifier Design

MIST Central Library Repository

Show simple item record

dc.contributor.author Kabir, Kazi Lutful
dc.contributor.author Kabir, Md. Rayhan
dc.contributor.author Islam, Md. Aminul
dc.date.accessioned 2015-06-30T05:42:13Z
dc.date.available 2015-06-30T05:42:13Z
dc.date.issued 2013-12
dc.identifier.uri http://hdl.handle.net/123456789/129
dc.description We are thankful to Almighty Allah for his blessings for the successful completion of our thesis. Our heartiest gratitude, profound indebtedness and deep respect go to our supervisor Dr. Hasan Sarwar, Professor and Head of the Department, CSE, United International University, House no. 80, Road 8/A, Sat Masjid Road, Dhanmondi, Dhaka, Bangladesh, for his constant supervision, affectionate guidance and great encouragement and motivation. His keen interest on the topic and valuable advices throughout the study was of great help in completing thesis. We are especially grateful to the Department of Computer Science and Engineering (CSE) of Military Institute of Science and Technology (MIST) for providing their all out support during the thesis work. Finally, we would like to thank our families and our course mates for their appreciable assistance, patience and suggestions during the course of our thesis. en_US
dc.description.abstract Character recognition is a very popular research field since 1950’s. A great deal of research work has been done for various languages specifically in case of English. The recognition of optical characters is known to be one of the earliest applications of Artificial Neural Networks. The use of artificial neural network simplifies development of an optical character recognition application, while achieving highest quality of recognition and good performance. Although Bangla is one of the most widely spoken languages (over 200 million people use Bangla as their medium of Communication) of the world, research is acute in recognition of Bangla characters. Under this context, an effort has been taken globally to computerize the Bangla language. Compared to English and other language scripts, one of the major stumbling blocks in Optical Character Recognition (OCR) of Bangla script is the large number of complex shaped character classes of Bangla alphabet. In addition to 50 basic character classes, there are nearly 160 complex shaped compound character classes in Bangla alphabet. Dealing with such a large variety of characters with a suitably designed feature set is a challenging problem. Uncertainty and imprecision is inherent in handwritten script. Moreover, such a large variety of complex shaped characters, some of which have close resemblance, makes the problem of OCR of Bangla characters more difficult. Considering the complexity of the problem, this research makes an attempt to develop a method for the recognition of Bangla characters using the artificial neural network. Pre-processing steps involves segmentation and binarization. Features are taken using different feature extraction procedures. Multilayered neural network is used in the spirit of back-propagation algorithm for classification as well as recognition of characters. To deal with immense variation and magnificent diversity of Bangla characters, this effort have widened the area for many research works to come to light and to bid fair to be accomplished. en_US
dc.description.sponsorship Department of Computer Science and Engineering, Military Institute of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Department of Computer Science and Engineering, Military Institute of Science and Technology en_US
dc.relation.ispartofseries Bachelor thesis in Computer Science and Engineering.;
dc.subject Bangla Character, Recognition,Artificial, Neural Network. en_US
dc.title Bangla Character Recognition using Artificial Neural Network Step:Classifier Design en_US
dc.type Thesis en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account