A Recognition-Based Approach to Segmenting Arabic Handwritten Text

Elnagar, Ashraf; Bentrcia, Rahima

doi:10.4236/jilsa.2015.74009

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…The technique searches for components in word segments that fit specified classes in its alphabet and divides them into their letters without breaking them into smaller units. This method also used in Elnagar and Bentrcia [29] at the first, the pre-processing is done for cleaning and extracting the features for the image, then the pre-processed text image was segmented into text lines, and the segmented text lines were segmented into words. For each segmented word, the thinning is applied, and then the main linked block was obtained in all thinned words, three kinds of regions features were extracted from these main linked blocks, seven factors are used to Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  determine the start and end of the cutting points, finally, the features were extracted from segmented regions and feed it artificial neural network to classify it as stroke or a character.…”

Section: Related Workmentioning

confidence: 99%

An approach to analysis of arabic text documents into text lines, words, and characters

Abdo

Abdu

Manza

et al. 2022

IJEECS

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Text line extraction from a text document image and segmenting it into isolate words and segmenting these words into individual characters are considered as one of the most critical processes in OCR systems development and turning the document into a searchable electronic representation, this paper presents a new approach to analyze the Arabic text documents, the proposed approach contains four steps, preprocessing, text line segmentation, word segmentation, character segmentation. The horizontal projection method are used to detect and extract the text line from preprocessed text documents image, in word segmentation step The space threshold are computed to determine the spaces among connected components in text line as within-word space or between-words space for segmenting the text line into isolate words, finally thinning method applied to find the skeleton of segmented word and analyses geometric characteristics of the characters to detect ligatures and characters. The proposed approach was tested and evaluated on a set of 115 text images, this set contains images from the KFUPM Handwritten Arabic TexT (KHATT) database and some images produced by the authors. The experiment results are extremely encouraging, with a success rate of 98.6% for lines segmentation, 96% for words segmentation, and 87.1% for characters segmentation.

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Section: Related Workmentioning

confidence: 99%

An approach to analysis of arabic text documents into text lines, words, and characters

Abdo

Abdu

Manza

et al. 2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…This was due to a proxy resolution, which shall take the appropriate decisions to determine the candidate segmentation points. The segments pass led to the identification that will invoke and apply the rules and agent pool on the unrecognized slides before passing to recognize again [17].…”

Section: Previous Workmentioning

confidence: 99%

Segmentation Accuracy for Offline Arabic Handwritten Recognition Based on Bounding Box Algorithm

Ismail¹

2016

CAE

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Character segmentation plays an important role in the Arabic optical character recognition (OCR) system, because the letters incorrectly segmented perform to unrecognized character. Accuracy of character recognition depends mainly on the segmentation algorithm used. The domain of off-line handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different segmentation algorithms for off-line Arabic handwriting recognition have been proposed and applied to various types of word images. This paper provides modify segmentation algorithm based on bounding box to improve segmentation accuracy using two main stages: preprocessing stage and segmentation stage. In preprocessing stage, used a set of methods such as noise removal, binarization, skew correction, thinning and slant correction, which retains shape of the character. In segmentation stage, the modify bounding box algorithm is done. In this algorithm a distance analysis use on bounding boxes of two connected components (CCs): main (CCs), auxiliary (CCs). The modified algorithm is presented and taking place according to three cases. Cut points also determined using structural features for segmentation character. The modified bounding box algorithm has been successfully tested on 450 word images of Arabic handwritten words. The results were very promising, indicating the efficiency of the suggested approach.

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“…The recognition method has superior performance capacity on its agents, and the artificial neural networks are correctly selected by applying the grouping rules that improve the detection of potential segmentation points [6]. It is vital to avoid over-segmentation mistakes while detecting segmentation points and characters by aiming at better results.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network

Ramdan

Omar

Faidzul

2017

International Journal on Advanced Science, Engineering and Information Technology

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Many methods of segmentation using detection of segmentation points or where the location of segmentation points is expected before the segmentation process, the validity of segmentation points is verified by using ANNs. In this paper apply a novel method to detect correctly of location segmentation points by detect of peaks with neural networks for Arabic word. This method employs baseline and peaks identification; where using two steps to segmenting text. Where peaks identification function is applied which at the subword segment level to frame the minimum and maximum peaks, and baseline detection. Where these two steps have led to the best result through the model depends on minimum peaks attained by utilising a stroke operator with a view to extracting potential points of segmentation, and determining the baseline procedure was developed to approximate the parameters. Where this method has yielded highly accurate positive results for Arabic characters' segmentation with four kinds of handwritten datasets as AHDB, IFN-ENIT, AHDB-FTR and ACDAR. Earlier results showed that the use of EDMS to MLP_ANN gives better results than GLCM and MOMENT in different groups and gives results of EDMS features on MNN with an accuracy level of 95.09% classifier for IFN-ENIT set of data.

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A Recognition-Based Approach to Segmenting Arabic Handwritten Text

Cited by 7 publications

References 15 publications

An approach to analysis of arabic text documents into text lines, words, and characters

An approach to analysis of arabic text documents into text lines, words, and characters

Segmentation Accuracy for Offline Arabic Handwritten Recognition Based on Bounding Box Algorithm

A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network

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