Word Extraction and Recognition in Arabic Handwritten Text

Aouadi, Nabil; Echi, Afef Kacem

doi:10.21700/ijcis.2016.103

Cited by 16 publications

(6 citation statements)

References 7 publications

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“…On the other hand, with structural features (Aouadi and Echi, 2016) authors extract words from Arabic historical documents by locating the touching letters, the junction points between two successive lines are located by the method of (Ouwayed and Belaïd, 2009) and those belonging to the same line are identified through a technique based on convex curve analysis. Tested on 1500 text lines, they reached an accuracy of 94%.…”

Section: Word Extractionmentioning

confidence: 99%

Text line and word detection and recognition of historical Arabic manuscripts

Hakim¹,

Belaid²

2023

Preprint

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The old Arabic manuscripts are highly sought-after documents but very difficult to access. Digitization, and thus handwriting recognition, is a beneficial way to make these resources accessible. This paper presents an end-to-end approach to the offline recognition of ancient manuscripts. First, a crucial pre-processing step is to extract text lines and words by applying transfer learning on YOLO (You Only Look Once) architecture. Thus the segmentation problem is treated as a detection problem. Then for the recognition of old handwritten words, we propose ensemble learning techniques based on recurrent neural networks associated with the Connectionist Temporal Classification layer (CTC) combined to convolution networks with Squeeze-and-Excitation blocks. The presented work accurately detects lines of text and words, even when overlapping or touching words are present, and correctly identifies those with multiple connected components. We evaluate this approach on a collection of 20 pages for text line detection. Moreover, we introduce a new consistent and accurate dataset for word detection and recognition. We have achieved promising results with 98.1% and 94.38% F1-measure on the text line and word detection, respectively, with a character error rate recognition of 8.27%.

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Section: Word Extractionmentioning

confidence: 99%

Text line and word detection and recognition of historical Arabic manuscripts

Hakim¹,

Belaid²

2023

Preprint

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“…3). The values within this template are sorted and the center of the sorted list is used to replace the template central pixel, several filters were examined however, a 3×3 median filter was selected because it gave us the preferable result (Aouadi and Echi, 2016).…”

Section: Noise Reductionmentioning

confidence: 99%

Developing an Arabic Handwritten Recognition System by Means of Artificial Neural Network

Ali¹,

Sadoon²

2019

J. Eng. Appl. Sci.

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The matter of handwritten text recognition is as yet a major challenge to mainstream researchers. A few ways deal with this challenge have been endeavored in the most recent years, for the most part concentrating on the English pre-printed or handwritten characters space. Consequently, the need to effort a research concerning to Arabic texts handwritten recognition. The Arabic handwriting presents unique technical difficulties because it is cursive, right to left in writing and the letters convert its shapes and structures when it is putted at initial, middle, isolation or at the end of words. In this study, the Arabic text recognition is developed and designed to recognize image of Arabic text/characters. The proposed model gets a single line of Arabic text, which convert and segments into words and then segments into letters. A multilayer feed forward neural network is trained to recognize these segments as characters. The final results indicate and clarify that the proposed system perform an effective accuracy of recognition rated up to 83% for Arabic text.

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“…In several investigation in the literature, researchers used several classifiers in order to recognize the Arabic handwritten words or the characters such as a methods of hidden markov models (HMM) [9]- [14], Knearest-neighbors (KNN) [15], [16], support vector machine (SVM) [17], neural networks [18]- [20], [5], [21], [22]. Otherwise the method of classification selected still owned weak points [23].…”

Section: Introductionmentioning

confidence: 99%

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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Word Extraction and Recognition in Arabic Handwritten Text

Cited by 16 publications

References 7 publications

Text line and word detection and recognition of historical Arabic manuscripts

Text line and word detection and recognition of historical Arabic manuscripts

Developing an Arabic Handwritten Recognition System by Means of Artificial Neural Network

Recognition of Arabic handwritten words using convolutional neural network

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