Recognition of handwritten cursive Arabic characters

Abuhaiba, Ibrahim S. I.; Mahmoud, Sabri A.; Green, R.J.

doi:10.1109/34.295912

Cited by 82 publications

(40 citation statements)

References 22 publications

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“…Since the potential spine P is a nonaccidentally aligned spine which generates the image, we can claim that for any point p, P − {p} is still a nonaccidentally aligned spine which reconstructs the image. This can be seen from the fact that if there are two different points p 1 and p 2 …”

Section: Lemma 4 Let P Be An Arbitrary Point On P If G( P) ∩ B Contmentioning

confidence: 95%

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General Ribbon-Based Thinning Algorithms for Stylus-Generated Images

Milun

Walters

Li³

1999

Computer Vision and Image Understanding

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Section: Lemma 4 Let P Be An Arbitrary Point On P If G( P) ∩ B Contmentioning

confidence: 95%

“…Researchers have used thinning algorithms in an attempt to recover the path from a stylus-generated image [1,2,6,38]. But researchers have found that the thinning results in practical applications are often so poor [13,14] that the technique has sometimes been dropped in favor of other encoding methods [20].…”

Section: Introductionmentioning

confidence: 99%

General Ribbon-Based Thinning Algorithms for Stylus-Generated Images

Milun

Walters

Li³

1999

Computer Vision and Image Understanding

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“…As mentioned previously, the first category of feature extraction included structural methods, e.g. lines, curves, loops, arcs, dots, junctions, word length, number, position and sequence of ascenders [2]. Some authors have presented a set of features in terms of a couple of clustering handwriting patterns and also combining them [7].…”

Section: Feature Extractionmentioning

confidence: 99%

Online versus offline Arabic script classification

Saba

Almazyad

Rehman

2015

Neural Comput & Applic

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Arabic script classification is a complex area of research in the field of computer vision. The issue of offline Arabic script classification has been a concern of many researchers interest currently as it is assumed that online Arabic script recognition is comparatively simple and significant achievements have been attained. Numerous researchers deal with these issues evolved in pre-processing and post-processing techniques of Arabic script and presented various approaches to improve its accuracy rate. However, offline Arabic script classification and its related issues are still fresh. In this paper, we focus on pre-processing to post-processing techniques and highlight several issues in each phase in order to highlight need of high classification performance for Arabic script classification (offline and online). Additionally, top experimental results are reported, discussed and compared, and current challenges are also discussed. Finally, online versus offline Arabic script recognition achievements are also compared.

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“…Another trend in Arabic handwriting recognition has been using fuzzy constrained character graph models. These models are fuzzily labeled graphs that represent characters [20]. This method has resulted in an accuracy of 73.6%.…”

Section: Survey On Ocr and Off-line Handwriting Recognitionmentioning

confidence: 99%

Recognition of Tibetan wood block prints with generalized hidden Markov and kernelized modified quadratic distance function

Hedayati

Chong

Keutzer

2011

Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data

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Abstract-Recognition of Tibetan wood block print is a difficult problem that has many challenging steps. We propose a two stage framework involving image preprocessing, which consists of noise removal and baseline detection, and simultaneous character segmentation and recognition by the aid of a generalized hidden Markov model (also known as gHMM). For the latter stage, we train a gHMM and run the generalized Viterbi algorithm on our image to decode observations. There are two major motivations for using gHMM. First, it incorporates a language model into our recognition system which in turn enforces grammar and disambiguates classification errors caused by printing errors and image noise. Second, gHMM solves the segmentation challenge. Simply put gHMM is an HMM where the emission model allows multiple consecutive observations to be mapped to the same state. For features of our emission model we apply line and circle Hough transform to stroke detection, and use classspecific scaling for feature weighing. With gHMM, we find KMQDF to be the most effective distance metric for discriminating character classes. The accuracy of our system is 90.03%.

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Recognition of handwritten cursive Arabic characters

Cited by 82 publications

References 22 publications

General Ribbon-Based Thinning Algorithms for Stylus-Generated Images

General Ribbon-Based Thinning Algorithms for Stylus-Generated Images

Online versus offline Arabic script classification

Recognition of Tibetan wood block prints with generalized hidden Markov and kernelized modified quadratic distance function

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