151817017605
Seller assumes all responsibility for this listing.
1985 ORIG 10 WHEEL CENTER CAP 1987 1988 Thunderbird Mustang 1986 1990 HOLE 1989 f0f1Hrqw
1989 10 1985 1987 HOLE CENTER 1986 1988 CAP Mustang 1990 WHEEL ORIG Thunderbird
1986 CAP Thunderbird WHEEL 10 ORIG HOLE Mustang CENTER 1985 1987 1990 1988 1989
Advertisement

Abstract: Optical Character Recognition is the process of converting an input text image into a machine encoded format. Different methods are used in OCR for different languages. T... View more
Abstract:
Optical Character Recognition is the process of converting an input text image into a machine encoded format. Different methods are used in OCR for different languages. The main steps of optical character recognition are pre-processing, segmentation and recognition. Recognizing handwritten text is harder than recognizing printed text. Convolutional Neural Network has shown remarkable improvement in recognizing characters of other languages. But CNNs have not been implemented for Malayalam handwritten characters yet. The proposed system uses Convolutional neural network to extract features. This is method different from the conventional method that requires handcrafted features that needs to be used for finding features in the text. We have tested the network against a newly constructed dataset of six Malayalam characters. This is method different from the conventional method that requires handcrafted features that needs to be used for finding features in the text.
Date of Conference: 10-11 March 2017
Date Added to IEEE Xplore: 17 July 2017
ISBN Information:
INSPEC Accession Number: 17042251
Publisher: IEEE
Conference Location: Coimbatore, India
Advertisement

I. Introduction

Deep learning Techniques has achieved top class performance in pattern recognition tasks. These include image recognition [1], [2], human face recognition [3], human pose estimation [4] and character recognition [5], [6]. These deep learning techniques have proved to outperform traditional methods for pattern recognition. Deep learning enables automation of feature extraction task. Traditional methods involve feature engineering which is to be done manually. This task of crafting features is time consuming and not very efficient. The features ultimately determine the effectiveness of the system. Deep learning methods outshine traditional methods by automatic feature extraction.

G3 Incl X 2 Adapter Lens Lens High Telephoto Canon Definition 2x PowerShot qwqC0P
Advertisement
Advertisement
Housing Vintage Fifty Fat Lower 50hp Outboard Evinrude Unit Motor g6FnqwUH