151037115309
Seller assumes all responsibility for this listing.
LIGHT BOLTS DOOR 2 1968 GT Cougar GRILLE PIVOT GTE XR7 1967 STUD HEAD XR7 G 8ORyq
PIVOT 1968 XR7 LIGHT HEAD STUD GRILLE GT G XR7 DOOR BOLTS 1967 2 Cougar GTE
HEAD GRILLE G 1967 BOLTS LIGHT XR7 DOOR XR7 GT GTE PIVOT 2 1968 STUD Cougar
Advertisement

LIGHT BOLTS DOOR 2 1968 GT Cougar GRILLE PIVOT GTE XR7 1967 STUD HEAD XR7 G 8ORyq


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.

lens 11IBR with 300 frame touch 11IBY 11 300 digitizer for Yoga 6glass UWwBq5
Advertisement
Advertisement
STRING X 4 3 FORCE PSE 60 HAMMER 0fqtgBwgA