Loading...

Implementation of Optical Character Recognition with Deep Learning

Samangouei, Mohammad | 2016

602 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 50655 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Bagheri Shouraki, Saeed
  7. Abstract:
  8. Optical character recognition (OCR) method has been used in converting printed text into editable text. OCR is very usefuland popular method in various applications. Accuracy of OCRn can be dependent on text preprocessing and segmentation algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, complex background of image etc. and Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations and a deep belief network (DBN) is a probabilistic , generative model madeup of multiple layers of hidden units. It can be considered a composition of simple learning modules that make up each layer We begin this paper with an introduction of Optical Character Recognition (OCR) method, and architecture of it. In this paper we used Neural Network Multilayer Perceptron (MLP). We concluded this paper by comparative study of this tool with other commercial OCR and explained how we can implement OCR with Neural Network MLP
  9. Keywords:
  10. Optical Character Recognition (OCR) ; Deep Learning ; Neural Network ; Multi-Layer Perceptron (MLP)

 Digital Object List

 Bookmark

No TOC