Autore Topic: Convolutional ensembles for Arabic Handwritten Character and Digit Recognition  (Letto 114 volte)

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Offline Flavio58

Convolutional ensembles for Arabic Handwritten Character and Digit Recognition
« Risposta #1 il: Ottobre 15, 2018, 08:15:28 pm »
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Convolutional ensembles for Arabic Handwritten Character and Digit Recognition

A learning algorithm is proposed for the task of Arabic Handwritten Character and Digit recognition. The architecture consists on an ensemble of different Convolutional Neural Networks. The proposed training algorithm uses a combination of adaptive gradient descent on the first epochs and regular stochastic gradient descent in the last epochs, to facilitate convergence. Different validation strategies are tested, namely Monte Carlo Cross-Validation and K-fold Cross Validation. Hyper-parameter tuning was done by using the MADbase digits dataset. State of the art validation and testing classification accuracies were achieved, with average values of 99.74% and 99.47% respectively. The same algorithm was then trained and tested with the AHCD character dataset, also yielding state of the art validation and testing classification accuracies: 98.60% and 98.42% respectively.
Source: Convolutional ensembles for Arabic Handwritten Character and Digit Recognition


Consulente in Informatica dal 1984

Software automazione, progettazione elettronica, computer vision, intelligenza artificiale, IoT, sicurezza informatica, tecnologie di sicurezza militare, SIGINT. 

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