Local patterns for offline Arabic handwritten recognition

Qawasmeh, Yasser; Awwad, Sari; Otoom, Ahmed Fawzi; Hanandeh, Feras; Abdallah, Emad E.

doi:10.1504/ijaip.2020.107017

Cited by 4 publications

(1 citation statement)

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“…Therefore, an appropriate approach is either to use a functional selection strategy to achieve the shortest range of functional attributes and thereby guarantee the highest precision or to use a reduction process to achieve smaller met feature vectors, which have linear combinations of 15 dimensions but have considerably low interrelationship attributes. In terms of their discriminatory power, their use a proper system of feature selection to recognize feature rankings and to iteratively fuse the features one by one according to their grades; otherwise, advancement inconsistency may occur [7]. The experimental research in this segment is therefore conducted in stages: (i) the quality of every feature is checked and the single feature test is named; (ii) every feature element in the test is related to weights; and (iii) numerous characteristics are chosen using an incremental responsive addition method to identify the best set of requirements for ideal identification precision.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Trainable System for Writer Identification of Arabic Handwriting

Saleem¹,

Abdulazeez²

2021

Computers, Materials &Amp; Continua

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Writer identification (WI) based on handwritten text structures is typically focused on digital characteristics, with letters/strokes representing the information acquired from the current research in the integration of individual writing habits/styles. Previous studies have indicated that a word's attributes contribute to greater recognition than the attributes of a character or stroke. As a result of the complexity of Arabic handwriting, segmenting and separating letters and strokes from a script poses a challenge in addition to WI schemes. In this work, we propose new texture features for WI based on text. The histogram of oriented gradient (HOG) features are modified to extract good features on the basis of the histogram of the orientation for different angles of texts. The fusion of these features with the features of convolutional neural networks (CNNs) results in a good vector of powerful features. Then, we reduce the features by selecting the best ones using a genetic algorithm. The normalization method is used to normalize the features and feed them to an artificial neural network classifier. Experimental results show that the proposed augmenter enhances the results for HOG features and ResNet50, as well as the proposed model, because the amount of data is increased. Such a large data volume helps the system to retrieve extensive information about the nature of writing patterns. The affective result of the proposed model for whole paragraphs, lines, and sub words is obtained using different models and then compared with those of the CNN and ResNet50. The whole paragraphs produce the best results in all models because they contain rich information and the model can utilize numerous features for different words. The HOG and CNN features achieve 94.2% accuracy for whole paragraphs with augmentation, 83.2% of accuracy for lines, and 78% accuracy for sub words. Thus, this work provides a system that can identify writers on the basis of their handwriting and builds a powerful model that can help identify writers on the basis of their sentences, words, and sub words.

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Section: Introductionmentioning

confidence: 99%

Hybrid Trainable System for Writer Identification of Arabic Handwriting

Saleem¹,

Abdulazeez²

2021

Computers, Materials &Amp; Continua

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A generic feature-independent pyramid multilevel model for Arabic handwriting recognition

Korichi

Slatnia

Aiadi

et al. 2022

Multimed Tools Appl

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Extended framework for Sindhi numerals OCR using gradient orientation histograms

Sanjrani

Baber

Bakhtyar

et al. 2022

IFS

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The accuracy on MINST dataset for roman numerals is already 99.65% . However, same models showed low accuracy on Sindhi numerals. It is because Sindhi numerals have high correlation between the shapes of the numerals. In this paper, correlation based template matching is used to analyze the shape ambiguity by identifying the dominant false positives (FP) and false negatives (FN) for every numeral. Furthermore, the Gradients Histogram Orientation (GOH) features are used to improve the accuracy of existing classifiers by image-to-image matching. The classical OCR using simple binary features are not sufficient to address the problems of shape ambiguity in Sindhi numerals, i.e., the shape of digits 2, , and 3, , are very similar. The raw pixel values are used as features for the classification in the first stage. In second stage, the input image is matched with the dominant FP and FN of the predicted class, and the final decision is made by the image-to-image matching based on GOH features. Decision based on image to image matching with dominant FP and FN increase the accuracy of the classifier. Support vector machine (SVM), K-nearest neighbor, and template based matching classifiers are used. The proposed extension substantially improves the accuracy of all mentioned classifiers.

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Local patterns for offline Arabic handwritten recognition

Cited by 4 publications

References 0 publications

Hybrid Trainable System for Writer Identification of Arabic Handwriting

Hybrid Trainable System for Writer Identification of Arabic Handwriting

A generic feature-independent pyramid multilevel model for Arabic handwriting recognition

Extended framework for Sindhi numerals OCR using gradient orientation histograms

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