Fast Zernike wavelet moments for Farsi character recognition

Broumandnia, Ali; Shanbehzadeh, Jamshid

doi:10.1016/j.imavis.2006.05.014

Cited by 67 publications

(27 citation statements)

References 22 publications

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“…Moments are an example of geometric features. Broumandnia and Shanbehzadeh [36] presents an approach for Farsi character recognition based on fast Zernike wavelet moments and artificial neural networks. Touj et al [37] presented a generalized Hough transform technique which is known for its ability to absorb the distortions in the document image and noise.…”

Section: Feature Extraction Phasementioning

confidence: 99%

Arabic character recognition using a Haar cascade classifier approach (HCC)

AbdelRaouf

Higgins

Pridmore

et al. 2015

Pattern Anal Applic

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Optical character recognition (OCR) shows great potential for rapid data entry, but has limited success when applied to the Arabic language. Traditional OCR problems are compounded by the nature of Arabic language and because the script is heavily connected. A machine learning, Haar cascade classifier (HCC) approach was introduced by Viola and Jones (Rapid object detection using a boosted cascade of simple features. Kauai, Hawaii, 2001) to achieve rapid object detection based on a boosted cascade of simple Haar-like features. Here, that approach is applied for the first time to suit Arabic glyph recognition. HCC approach eliminates problematic steps in the preprocessing and recognition phases and, most importantly, character segmentation stage. A classifier was produced for each of the 61 Arabic glyphs that exist after the removal of diacritical marks (dots). These classifiers were trained and tested on some 2,000 images each. The system was tested with real text images and produces a recognition rate for Arabic glyphs of 87 %. The technique gives good results relative to those achieved using a commercial Arabic OCR application and existing state-of-the-art research application.

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Section: Feature Extraction Phasementioning

confidence: 99%

Arabic character recognition using a Haar cascade classifier approach (HCC)

AbdelRaouf

Higgins

Pridmore

et al. 2015

Pattern Anal Applic

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“…Other important properties include robustness against transformational noise and excellent reconstruction capabilities. Owing to these properties, the ZMs were applied in the fields of character recognition [3], watermarking [4,5], image retrieval [6], texture retrieval [7], face recognition [8] and image reconstruction [9]. Pseudo-Zernike moments (PZMs) were given by Bhatia and Wolf [10].…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Rotation Invariance in Moment Coefficients

Ranade¹,

Anand²

2015

IJCA

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Moments can be viewed as powerful image descriptors that capture global characteristics of an image. The magnitude of the moment coefficients is said to be invariant under geometrical transformations like rotation which makes them suitable for most of the recognition applications. But in practice, the invariance of moment coefficients is compromised due to the errors in computation. This paper presents an empirical study of some popularly used moment functions to find out the robust coefficients under rotation. The selected robust coefficients are used in face recognition under in-plane rotation. Experimental results demonstrate that the performance of the proposed method comes at par with the performance of the traditional method by using lesser number of moment coefficients and thus results in significant saving in the feature extraction time.

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“…In the on-line method, recognition of handwriting is done on the characters captured using an input device such as digitized pen [3] while in the off-line method, identification is performed on the image a character [4].…”

Section: Introductionmentioning

confidence: 99%