End-to-end system for printed Amazigh script recognition in document images

Aharrane, Nabil; Dahmouni, Abdellatif; Ensah, Karim El Moutaouakil; Satori, Khalid

doi:10.1109/atsip.2017.8075520

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Next, the final matrix is flattened to transform the matrix into vectors. The final convolution layers are connected to a fully connected artificial neural network [69][70][71][72], which consists of three types of layers: the input layer, the hidden layers, and the decision layer (or classification layer). The input layer performs no processing but represents the resulting vectors to the neural network.…”

Section: Deep Cnn Classification: Int-fup-cnn Vs Classical Pooling-cnnmentioning

confidence: 99%

INT-FUP: Intuitionistic Fuzzy Pooling

Rajafillah,

El Moutaouakil,

Patriciu

et al. 2024

Mathematics

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Convolutional Neural Networks (CNNs) are a kind of artificial neural network designed to extract features and find out patterns for tasks such as segmentation, recognizing objects, and drawing up classification. Within a CNNs architecture, pooling operations are used until the number of parameters and the computational complexity are reduced. Numerous papers have focused on investigating the impact of pooling on the performance of Convolutional Neural Networks (CNNs), leading to the development of various pooling models. Recently, a fuzzy pooling operation based on type-1 fuzzy sets was introduced to cope with the local imprecision of the feature maps. However, in fuzzy set theory, it is not always accurate to assume that the degree of non-membership of an element in a fuzzy set is simply the complement of the degree of membership. This is due to the potential existence of a hesitation degree, which implies a certain level of uncertainty. To overcome this limitation, intuitionistic fuzzy sets (IFS) were introduced to incorporate the concept of a degree of hesitation. In this paper, we introduce a novel pooling operation based on intuitionistic fuzzy sets to incorporate the degree of hesitation heretofore neglected by a fuzzy pooling operation based on classical fuzzy sets, and we investigate its performance in the context of image classification. Intuitionistic pooling is performed in four steps: bifuzzification (by the transformation of data through the use of membership and non-membership maps), first aggregation (through the transformation of the IFS into a standard fuzzy set, second aggregation (through the transformation and use of a sum operator), and the defuzzification of feature map neighborhoods by using a max operator. IFS pooling is used for the construction of an intuitionistic pooling layer that can be applied as a drop-in replacement for the current, fuzzy (type-1) and crisp, pooling layers of CNN architectures. Various experiments involving multiple datasets demonstrate that an IFS-based pooling can enhance the classification performance of a CNN. A benchmarking study reveals that this significantly outperforms even the most recent pooling models, especially in stochastic environments.

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Section: Deep Cnn Classification: Int-fup-cnn Vs Classical Pooling-cnnmentioning

confidence: 99%