Offline Arabic Handwriting Recognition Using BLSTMs Combination

Jemni, Sana Khamekhem; Kessentini, Yousri; Kanoun, Slim; Ogier, Jean-Marc

doi:10.1109/das.2018.54

Cited by 12 publications

(13 citation statements)

References 21 publications

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“…In the field of automatic handwriting recognition, applying a combination of hybrid cooperative classifiers together is a thought-provoking issue to enhance the overall system performance [8]. There are two main categories of combination techniques: feature fusion and decision fusion.…”

Section: Literature Review a Concepts And Analysismentioning

confidence: 99%

“…Decision fusion techniques integrate different classifiers' decisions; each classifier is trained on a different set of features. More recent approaches [8], [17], [18], stated that this high-level combination strategy improves the performance of handwriting recognition systems.…”

Section: Literature Review a Concepts And Analysismentioning

confidence: 99%

“…One of the important concepts related to building robust recognition systems is feature localization. Different automatic Arabic recognition systems [8],…”

Section: Literature Review a Concepts And Analysismentioning

confidence: 99%

“…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. Some recent approaches [33]- [35], applied Convolutional Neural Network (CNN) to automatically extract features from raw images.…”

Section: Features Extractionmentioning

confidence: 99%

“…It is a sort of feed-forward network that can extract valuable topological features from raw images. Instead of extracting the traditional features, neural networks are capable of extracting features from large-scale raw data [8]. Recent surveys stated the effectiveness of deep learning techniques [9]- [11], in the field of character recognition.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Literature Review a Concepts And Analysismentioning

confidence: 99%

“…One of the important concepts related to building robust recognition systems is feature localization. Different automatic Arabic recognition systems [8],…”

Section: Literature Review a Concepts And Analysismentioning

confidence: 99%