A hybrid recognition system for off-line handwritten characters

Katiyar, Gauri; Mehfuz, Shabana

doi:10.1186/s40064-016-1775-7

Cited by 24 publications

(10 citation statements)

References 33 publications

(29 reference statements)

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“…This paper is an extension of previous work done by authors in the field of off-line handwritten character recognition [3] [4] [5].…”

Section: Introductionmentioning

confidence: 83%

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Katiyar¹

2017

AJNNA

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Selection of classifiers and feature extraction methods has a prime role in achieving best possible classification accuracy in character recognition system. Issues of character recognition system related to choice of classifiers and feature extraction methods can be resolved through these objectives. In this proposed work an efficient Support Vector Machine based off-line handwritten character recognition system has been developed. The experiments have been performed using well known standard database acquired from CEDAR, also seven different approaches of feature extraction techniques have been proposed to construct the final feature vector. It is evident from the experimental results that the performance of Support Vector Machine outperforms other state of art techniques reported in literature.

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“…This paper is an extension of previous work done by authors in the field of off-line handwritten character recognition [3] [4] [5].…”

Section: Introductionmentioning

confidence: 83%

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Katiyar¹

2017

AJNNA

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“…Katiyar and Mehfuz [13] developed a hybrid recognition system to recognize offline handwritten characters. They merged multiple features extracted using seven different approaches.…”

Section: Related Workmentioning

confidence: 99%

Devanagari ancient documents recognition using statistical feature extraction techniques

2019

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Devanagari ancient document recognition process is drawing a lot of consideration from researchers nowadays. These ancient documents contain a wealth of knowledge. However, these documents are not available to all because of their fragile condition. A Devanagari ancient manuscript recognition system is designed for digital archiving. This system includes image binarization, character segmentation and recognition phases. It incorporates automatic recognition of scanned and segmented characters. Segmented characters may include basic characters (vowels and consonants), modifiers (matras) and various compound characters (characters formed by joining more than one basic characters). In this paper, handwritten Devanagari ancient manuscripts recognition system has been presented using statistical features extraction techniques. In feature extraction phase, intersection points, open endpoints, centroid, horizontal peak extent and vertical peak extent features are extracted. For classification, Convolutional Neural Network, Neural Network, Multilayer Perceptron, RBF-SVM and random forest techniques are considered in this work. Various feature extraction and classification techniques are considered and compared to the recognition of basic characters segmented from Devanagari ancient manuscripts. A data set, of 6152 pre-segmented samples of Devanagari ancient documents, is considered for experimental work. Authors have achieved 88.95% recognition accuracy using a combination of all features and a combination of all classifiers considered in this work by a simple majority voting scheme.

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“…OCR technology enables us to convert the scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data. The classical techniques for OCR are Template matching, Statistical and Structural methods, while the soft computing techniques such as Artificial Neural Networks (ANNs), Fuzzy logic and Evolutionary computing techniques are also employed [12,13].…”

Section: Optical Character Recognition(ocr) Processmentioning

confidence: 99%

Development of Machine Vision System for Automatic Inspection of Vehicle Identification Number

Ramshankar¹,

Deivanathan²

2018

IJEM

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The vision system is developed to reduce the human effort and improve productivity in the Vehicle Quality Assurance (VQA) shop for inspection of a car bearing a Vehicle Identification Number (VIN), assigned to it at the assembly shop. This project work is carried out in association with M/s Renault Nissan Automotive India Pvt Ltd, Chennai. The vision system consists of a camera fixed on a pan-tilt camera frame and an Optical Character Recognition (OCR) software. The camera frame is mounted on a belt conveyor with remote control of forward, backward and tilting motion. The image of VIN present at the car door is captured through a digital camera placed adjacent to the car. The characters in the VIN image thus obtained are extracted using MATLAB, with configurable OCR software. Template matching method is followed in the OCR process. The MATLAB code can overcome trivial issues in VIN image inspection at the quality shop. Development of a graphic user interface to the software is also described.

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A hybrid recognition system for off-line handwritten characters

Cited by 24 publications

References 33 publications

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Off-Line Handwritten Character Recognition System Using Support Vector Machine

Devanagari ancient documents recognition using statistical feature extraction techniques

Development of Machine Vision System for Automatic Inspection of Vehicle Identification Number

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