Authors:
Takwa Gader
;
Issam Chibani
and
Afef Echi
Affiliation:
National Superior School of Engineering, University of Tunis, LR: LATICE, Tunisia
Keyword(s):
Recognition of Arabic Handwritten, Segmentation, Features Extraction, Recurrent Neural Network, ConvLSTM.
Abstract:
This work is released in the field of automatic document recognition, specifically offline Arabic handwritten recognition. Arabic writing is cursive and recognized as quite complex compared to handwritten Latin script: dependence on context, difficulties with segmentation, a large number of words, variations in the style of the writing, inter- and intra-word overlap, etc. Few works exist concerning recognizing Arabic manuscripts without constraint, which motivates us to move towards this type of document based on an approach based on deep learning. It is one of the machine-learning approaches reputed to be effective for classification problems. It is about conceiving and implementing an end-to-end system: a convolutional long-short-term memory (ConvLSTM ). It consists of a recurrent neural network for spatiotemporal prediction with convolutional structures that allow feature extraction. A connectionist temporal classification output layer processes the returned result. Our model is t
rained and tested using the IFN/ENIT database. We were able to achieve a recognition rate of 99.01%.
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