An off-line cursive handwriting recognition system

Senior, Andrew W.; Robinson, Anthony J.

doi:10.1109/34.667887

Cited by 193 publications

(99 citation statements)

References 12 publications

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“…A well known technique for determining the direction of text lines is the Projection Profile technique [1][2][3] which creates a histogram crossing an entire text. In [6], the process of locating lines of text is focused only on the gap between the lines. Projection profile with local minima is used to detect the separating lines as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Using local maxima profile and Piece-Wise technique for line segmentation on Thai handwritten historical documents

Sangsawad

Chamchong

Fung

2011

2011 International Conference on Machine Learning and Cybernetics

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Abstract:This paper presents a new approach for segmenting text lines on Thai handwritten documents. The proposed technique is based on an Adaptive Local Connectivity Map concept using Piece-Wise Separating Lines. The algorithm is designed to solve problems in handwritten documents such as fluctuating text lines. Moreover, local maxima projection profile is used for enhancing the speed of extraction. The proposed algorithm consists of four steps. Firstly, Otsu algorithm is used to binarize the source image. Second, Piece-Wise Separating Lines is applied to derive the Adaptive Local Connectivity Map to show mask text lines. In the third step, local maxima projection profile is used as a guideline for extracting text lines. Finally, contour algorithm is used to identify the interested mask text line. The interested mask text is used to map with text image in order to extract the text lines. Analysis of experimental results on the King Rama 5 archive data indicated that the method has achieved a correct rate of 85.7%.

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Section: Introductionmentioning

confidence: 99%

Using local maxima profile and Piece-Wise technique for line segmentation on Thai handwritten historical documents

Sangsawad

Chamchong

Fung

2011

2011 International Conference on Machine Learning and Cybernetics

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“…It allows for a determination of the orientation of the writing direction. Our idea for the computation of the baseline is derived from the approach of Senior and Robinson [2]. In contrast to [2], our algorithm abstains from cutting descenders in the first step, because for our test samples no horizontal slope for the sample can be guaranteed.…”

Section: Description Of the New Featuresmentioning

confidence: 99%

“…Our idea for the computation of the baseline is derived from the approach of Senior and Robinson [2]. In contrast to [2], our algorithm abstains from cutting descenders in the first step, because for our test samples no horizontal slope for the sample can be guaranteed. Instead our first step is to identify the point with the smallest y-coordinate in each column (of the x-y matrix representing the handwritten sample), i.e.…”

Section: Description Of the New Featuresmentioning

confidence: 99%

Security of Features Describing the Visual Appearance of Handwriting Samples Using the Bio-hash Algorithm of Vielhauer against an Evolutionary Algorithm Attack

Hasselberg

Zimmermann

Kraetzer

et al. 2013

Communications and Multimedia Security

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Abstract.To improve the security and stability of biometric handwriting samples a Bio-Hash algorithm for handwriting was introduced in [1]. It utilizes features to describe how the sample was written, but the current set of features does not characterize the visual appearance of the sample itself. In this paper we present a set of new features derived from handwriting forensics and OCR algorithms to address this issue. Furthermore, here the security of the old and new sets of features is evaluated for their resilience against a new, fully automated attack trying to compute raw data matching a given hash vector.The main contributions of this paper are: The introduction of new features with a potential to increase the attack resilience of the Bio-Hash algorithm, and, an improvement of the attack approach from [6] to produce more realistic looking synthetic handwriting signals.

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“…Many new approaches have been proposed concerning integrated systems [4], specific applications [5,16], and various languages [6]. Through co-operation and competition a lot of work has been done not only in recognition but also in the required preprocessing stages of an OCR system [7].…”

Section: Introductionmentioning

confidence: 99%

An unconstrained handwriting recognition system

Kavallieratou¹,

Fakotakis²,

Kokkinakis

2002

International Journal on Document Analysis and Recognition

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Abstract. In this paper, an integrated offline recognition system for unconstrained handwriting is presented. The proposed system consists of seven main modules: skew angle estimation and correction, printedhandwritten text discrimination, line segmentation, slant removing, word segmentation, and character segmentation and recognition, stemming from the implementation of already existing algorithms as well as novel algorithms. This system has been tested on the NIST, IAM-DB, and GRUHD databases and has achieved accuracy that varies from 65.6% to 100% depending on the database and the experiment.

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An off-line cursive handwriting recognition system

Cited by 193 publications

References 12 publications

Using local maxima profile and Piece-Wise technique for line segmentation on Thai handwritten historical documents

Using local maxima profile and Piece-Wise technique for line segmentation on Thai handwritten historical documents

Security of Features Describing the Visual Appearance of Handwriting Samples Using the Bio-hash Algorithm of Vielhauer against an Evolutionary Algorithm Attack

An unconstrained handwriting recognition system

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