Text Line and Word Extraction of Arabic Handwritten Documents

Lamsaf, Asmae; Aitkerroum, Mounir; Boulaknadel, Siham; Fakhri, Yadolah

doi:10.1007/978-3-030-11196-0_42

Cited by 5 publications

(4 citation statements)

References 19 publications

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Section: Methods 21 Motivationmentioning

confidence: 99%

“…There are several authors used CNN for the stage of the extraction of features of the images [30], [34]. There are others which combines CNN and other classifiers (SVM and HMM) to classify the handwritten Arabic words [30], [34]. In our work, CNN used for extracting the feature and classification steps of Arabic handwritten words recognition.…”

Section: Methods 21 Motivationmentioning

confidence: 99%

“…The global thresholding used for calculating one threshold to the whole image. The pixels that are above the threshold affect 0 and others affect the value 1 [34].…”

Section: Preprocessing 231 Binarizationmentioning

confidence: 99%

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Recognition of Arabic handwritten words using convolutional neural network

Lamsaf

Kerroum

Boulaknadel

et al. 2022

IJEECS

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<span>A new method for recognizing automatically Arabic handwritten words was presented using convolutional neural network architecture. The proposed method is based on global approaches, which consists of recognizing all the words without segmenting into the characters in order to recognize them separately. Convolutional neural network (CNN) is a particular supervised type of neural network based on multilayer principle; our method needs a big dataset of word images to obtain the best result. To optimize our system, a new database was collected from the benchmarking Arabic handwriting database using the pre-processing such as rotation transformation, which is applied on the images of the database to create new images with different features. The convolutional neural network applied on our database that contains 40320 of Arabic handwritten words (26880 images for training set and 13440 for test set). Thus, different configurations on a public benchmark database were evaluated and compared with previous methods. Consequently, it is demonstrated a recognition rate with a success of 96.76%.</span>

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Section: Methods 21 Motivationmentioning

confidence: 99%

Section: Methods 21 Motivationmentioning

confidence: 99%

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Recognition of Arabic handwritten words using convolutional neural network

Lamsaf

Kerroum

Boulaknadel

et al. 2022

IJEECS

Self Cite

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“…In order to segment of text line, we used the method presented in Lamsaf et al [32] method used horizontal projection approach. The method first calculates the histogram of the horizontal projection of the text document image, and then impacts the value of 0 for lines with sums less than or equal to the threshold l=12.…”

Section: Text Line Segmentationmentioning

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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