Using features of local densities, statistics and HMM toolkit (HTK) for offline Arabic handwriting text recognition

Moubtahij, Hicham El; Halli, Akram; Satori, Khalid

doi:10.1016/j.jesit.2016.07.005

Cited by 25 publications

(18 citation statements)

References 7 publications

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“…References [19]- [21], applied sliding window-based feature extraction techniques to do localization. It was also stated [22] that applying multiple sliding windows achieved superior recognition rates.…”

Section: Literature Review a Concepts And Analysismentioning

confidence: 99%

“…Other recent systems [5], [25], segment words to different categories of segments: core shapes, sub-core shapes and diacritics. Still others [19], [26]- [28], recognize words in a holistic manner without any prior segmentation. This holistic way avoids recognition errors that could be caused due to wrong segmentation, but it requires robustness of features.…”

Section: B Categorization Based On Segmentation Approachesmentioning

confidence: 99%

“…This holistic way avoids recognition errors that could be caused due to wrong segmentation, but it requires robustness of features. The proposed holistic approaches [19], [26]- [28], stated that statistical and structural features achieved this required robustness. On the other hand, some proposed analytical approaches [19], [22], [26], are based on modeling each word by concatenating its character models.…”

Section: B Categorization Based On Segmentation Approachesmentioning

confidence: 99%

“…Different types of features were extracted for Arabic handwriting recognition. Some approaches [19], [26], [27], computed a combination of statistical and structural features, whereas others [23], [30], [31], computed skeleton-based features. Distribution of concavity features was obtained in different approaches [8], [24], [32], and has been proved effective in representing Arabic handwriting.…”

Section: Features Extractionmentioning

confidence: 99%

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Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

2020

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Recently, deep learning techniques demonstrated efficiency in building better performing machine learning models which are required in the field of offline Arabic handwriting recognition. Our ancient civilizations presented valuable handwritten manuscripts that need to be documented digitally. If we compared between Latin and the isolated Arabic character recognition, the latter is much more challenging due to the similarity between characters, and the variability of the writing styles. This paper proposes a multistage cascading system to serve the field of offline Arabic handwriting recognition. The approach starts with applying the Hierarchical Agglomerative Clustering (HAC) technique to split the database into partially interrelated clusters. The interrelations between the constructed clusters support representing the database as a big search tree model and help to attain a reduced complexity in matching each test image with a cluster. Cluster members are then ranked based on our new proposed ranking algorithm. This ranking algorithm starts with computing Pyramid Histogram of Oriented Gradients (PHoG), and is followed by measuring divergence by Kullback-Leibler method. Eventually, the classification process is applied only to the highly ranked matching classes. A comparative study is made to assess the effect of six different deep Convolution Neural Networks (DCNNs) on the final recognition rates of the proposed system. Experiments are done using the IFN/ENIT Arabic database. The proposed clustering and ranking stages lead to using only 11% of the whole database in classifying test images. Accordingly, more reduced computation complexity and more enhanced classification results are achieved compared to recent existing systems. INDEX TERMS Agglomerative hierarchical clustering, deep convolutional neural network, Kullback-Leibler divergence, offline arabic handwritting recognition, pyramid histogram of gradients.

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Section: Literature Review a Concepts And Analysismentioning

confidence: 99%

Section: B Categorization Based On Segmentation Approachesmentioning

confidence: 99%

Section: B Categorization Based On Segmentation Approachesmentioning

confidence: 99%

Section: Features Extractionmentioning

confidence: 99%

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Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

2020

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“…• Word slant feature: in order to determine the word slant feature, we use the same technique described in [33]. [34] method for determining the average stroke width of the word.…”

Section: Base Layermentioning

confidence: 99%

Predicting the Big Five personality traits from handwriting

Gavrilescu

Vizireanu

2018

J Image Video Proc.

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We propose the first non-invasive three-layer architecture in literature based on neural networks that aims to determine the Big Five personality traits of an individual by analyzing offline handwriting. We also present the first database in literature that links the Big Five personality type with the handwriting features collected from 128 subjects containing both predefined and random texts. Testing our novel architecture on this database, we show that the predefined texts add more value if enforced on writers in the training stage, offering accuracies of 84.4% in intra-subject tests and 80.5% in inter-subject tests when the random dataset is used for testing purposes, up to 7% higher than when random datasets are used in the training phase. We obtain the highest prediction accuracy for Openness to Experience, Extraversion, and Neuroticism (over 84%), while for Conscientiousness and Agreeableness, the prediction accuracy is around 77%. Overall, our approach offers the highest accuracy compared with other state-of-the-art methods and results are computed in maximum 90 s, making the approach faster than the questionnaire or psychological interviews currently used for determining the Big Five personality traits. Our research also shows there are relationships between specific handwriting features and prediction with high accuracy of specific personality traits and this can be further exploited for improving, even more, the prediction accuracy of the proposed architecture.

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