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MANUAL FACTORY ASSEMBLY 442 1973 OLDSMOBILE CUTLASS gCw0npq
CUTLASS ASSEMBLY MANUAL 1973 442 OLDSMOBILE FACTORY
OLDSMOBILE ASSEMBLY MANUAL 442 FACTORY CUTLASS 1973
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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
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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.

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