Robust baseline-independent algorithms for segmentation and reconstruction of Arabic handwritten cursive script

Mostafa, Khaled; Darwish, Ahmed M.

doi:10.1117/12.335805

Cited by 6 publications

(3 citation statements)

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“…This image portion is removed from the overall image the process repeated until all characters have been recognized. Mosfeq (1996) applied the concept of centering to segment the characters in which a character is correctly segmented if it appeared in the centre of a large window, regardless of what else appeared in that window.…”

Section: Recognition-based Segmentation Methodsmentioning

confidence: 99%

Segmentation of Arabic Characters

Zeki

Zakaria

Liong

2011

International Journal of Technology Diffusion

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The cursive nature of Arabic writing is the main challenge to Arabic Optical Character Recognition developer. Methods to segment Arabic words into characters have been proposed. This paper provides a comprehensive review of the methods proposed by researchers to segment Arabic characters. The segmentation methods are categorized into nine different methods based on techniques used. The advantages and drawbacks of each are presented and discussed. Most researchers did not report the segmentation accuracy in their research; instead, they reported the overall recognition rate which did not reflect the influence of each sub-stage on the final recognition rate. The size of the training/testing data was not large enough to be generalized. The field of Arabic Character Recognition needs a standard set of test documents in both image and character formats, together with the ground truth and a set of performance evaluation tools, which would enable comparing the performance of different algorithms. As each method has its strengths, a hybrid segmentation approach is a promising method. The paper concludes that there is still no perfect segmentation method for ACR and much opportunity for research in this area.

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Section: Recognition-based Segmentation Methodsmentioning

confidence: 99%

Segmentation of Arabic Characters

Zeki

Zakaria

Liong

2011

International Journal of Technology Diffusion

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“…Mostafa and Darwish presented baseline-independent algorithms to detect lines and words, to segment words into primitives and to extract diacritics in handwritten text (1999) [76]. Using the chain code representation, the segmentation algorithm oversegmented words, then applied rules to remove extra points.…”

Section: Table 5 Segmentation-based and Holistic Approachesmentioning

confidence: 99%

Offline Arabic handwriting recognition: a survey

Lorigo

Govindaraju

2006

IEEE Trans. Pattern Anal. Machine Intell.

407

174

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The automatic recognition of text on scanned images has enabled many applications such as searching for words in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images. This paper provides a comprehensive review of these methods. It is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed. It includes background on the field, discussion of the methods, and future research directions.

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“…Recognition systems generally use a holistic or segmentation-based method. Segmentation-based systems [4]- [7] break a word into parts called segments, which can be a letter or even a sub-letter, and try to recognize each segment individually and concatenate the results. On the other hand, holistic approaches see a word as a whole, and apply a recognition phase [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Sub-word-based Offline Handwritten Farsi Word Recognition Using Recurrent Neural Network

Ghadikolaie¹,

Kabir²,

Razzazi³

2016

ETRI J

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In this paper, we present a segmentation‐based method for offline Farsi handwritten word recognition. Although most segmentation‐based systems suffer from segmentation errors within the first stages of recognition, using the inherent features of the Farsi writing script, we have segmented the words into sub‐words. Instead of using a single complex classifier with many (N) output classes, we have created N simple recurrent neural network classifiers, each having only true/false outputs with the ability to recognize sub‐words. Through the extraction of the number of sub‐words in each word, and labeling the position of each sub‐word (beginning/middle/end), many of the sub‐word classifiers can be pruned, and a few remaining sub‐word classifiers can be evaluated during the sub‐word recognition stage. The candidate sub‐words are then joined together and the closest word from the lexicon is chosen. The proposed method was evaluated using the Iranshahr database, which consists of 17,000 samples of Iranian handwritten city names. The results show the high recognition accuracy of the proposed method.

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Robust baseline-independent algorithms for segmentation and reconstruction of Arabic handwritten cursive script

Cited by 6 publications

References 0 publications

Segmentation of Arabic Characters

Segmentation of Arabic Characters

Offline Arabic handwriting recognition: a survey

Sub-word-based Offline Handwritten Farsi Word Recognition Using Recurrent Neural Network

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