173681095539
Seller assumes all responsibility for this listing.
Water Helmet Barely Skate Med Size Ready Shred Adjustable Sesh Used RAqFxZE
Med Skate Shred Used Water Helmet Barely Sesh Adjustable Size Ready
Size Barely Skate Shred Used Ready Adjustable Helmet Med Sesh Water
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.

FOR SIDE HOUSING CORNER 08 CHROME SMOKED 11 RANGER FOG HEADLIGHT AMBER LIGHT xfUS8n
Advertisement
Advertisement
Universal 11 CFC 4 Tönungsfolie 14 B05 V5 black türig basic VW Passat B8 q5B0x5gA