A New Adaptive Zoning Technique for Handwritten Digit Recognition

Impedovo, S.; Mangini, F; Pirlo, G.

doi:10.1007/978-3-642-41181-6_10

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…For example, simplified versions of the shape context descriptor have been used in example-based keyword spotting architectures by Lladós and Sánchez [27] or by Fernández et al [11]. Zoning-based characteristics have also been widely used to represent word images holistically, e.g., [17,20]. A combination of Histogram of Oriented Gradients (HOG) and Local Binary Pattern descriptors has been proposed by Kovalchuk et al in [21] in a segmentation-free keyword spotting scenario.…”

Section: Introductionmentioning

confidence: 98%

A study of Bag-of-Visual-Words representations for handwritten keyword spotting

Aldavert

Rusiñol

Toledo

et al. 2015

IJDAR

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The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014.

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Section: Introductionmentioning

confidence: 98%

A study of Bag-of-Visual-Words representations for handwritten keyword spotting

Aldavert

Rusiñol

Toledo

et al. 2015

IJDAR

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“…Finally, Impedovo et al introduced a genetic algorithm based clustering approach using zoning features [19] whereas an adaptive zoning techniques for handwritten digit recognition are presented [20,21] where the features are extracted according to an optimal zoning distribution.…”

Section: Introductionmentioning

confidence: 99%

Novel feature extraction technique for the recognition of handwritten digits

Boukharouba

Bennia

2017

Applied Computing and Informatics

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This paper presents an efficient handwritten digit recognition system based on support vector machines (SVM). A novel feature set based on transition information in the vertical and horizontal directions of a digit image combined with the famous Freeman chain code is proposed. The main advantage of this feature extraction algorithm is that it does not require any normalization of digits. These features are very simple to implement compared to other methods. We evaluated our scheme on 80,000 handwritten samples of Persian numerals and we have achieved very promising results. Ó 2015 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).

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“…Spatial and transform domain are two major categories in Feature extraction techniques. Invariant moments [1], projections [2,64], zoning feature [3], an adaptive zoning techniques [4], [5], [55] and [6],gradient based features [7][8] [10] are some of examples of spatial domain techniques. In spatial domain statistical or structural or both [9][10][11][12][13] properties can be used for extracting features.…”

Section: Introductionmentioning

confidence: 99%

A Robust Zonal Fractal Dimension Method for the Recognition of Handwritten Telugu Digits

Subrahmanyam¹,

Kumar²,

Reddy³

2018

IJIGSP

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Recognition of handwritten digits is most challenging sub task of character recognition due to various shapes, sizes, large variation in writing styles from person to person and also similarity in shapes of different digits. This paper presents a robust Telugu language handwritten digit recognition system. The Telugu language is most popular and one of classical languages of India. This language is spoken by more than 80 million people. The proposed method initially performs preprocessing on input digit pattern for removing noise, slat correction, size normalization and thinning. This paper divides the preprocessed Telugu handwritten digits into four differential zones of 2x2, 3x3, 4x4 and 6x6 pixels and extracts 65 features using Fractal dimension (FD) from each zone. The proposed zonal fractal dimension (ZFD) method uses, Feed forward backward propagation neural network (FFBPNN) for classifying the digits with learning rate of 0.01 and sigmoid function as an activation function on extracted 65 features. This paper evaluated the efficiency of the proposed method based on 5000 Telugu handwritten digit samples, each consists of ten digits from different groups of people and totally 50,000 samples. The performance of classification of the proposed method also evaluated using statistical parameters like recall, precision, Fmeasure and accuracy.

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A New Adaptive Zoning Technique for Handwritten Digit Recognition

Cited by 5 publications

References 17 publications

A study of Bag-of-Visual-Words representations for handwritten keyword spotting

A study of Bag-of-Visual-Words representations for handwritten keyword spotting

Novel feature extraction technique for the recognition of handwritten digits

A Robust Zonal Fractal Dimension Method for the Recognition of Handwritten Telugu Digits

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