Paper Info
Title
Improving Recurrent Neural Networks For Offline Arabic Handwriting Recognition By Combining Different Language Models
Abstract
In handwriting recognition, the design of relevant features is very important, but it is a daunting task. Deep neural networks are able to extract pertinent features automatically from the input image. This drops the dependency on handcrafted features, which is typically a trial and error process. In this paper, we perform an exhaustive experimental evaluation of learned against handcrafted features for Arabic handwriting recognition task. Moreover, we focus on the optimization of the competing full-word based language models by incorporating different characters and sub-words models. We extensively investigate the use of different sub-word-based language models, mainly characters, pseudo-words, morphemes and hybrid units in order to enhance the full-word handwriting recognition system for Arabic script. The proposed method allows the recognition of any out of vocabulary word as an arbitrary sequence of sub-word units. The KHATT database has been used as a benchmark for the Arabic handwriting recognition. We show that combining multiple language models enhances considerably the recognition performance for a morphologically rich language like Arabic. We achieve the state-of-the-art performance on the KHATT dataset.
Year
DOI
Venue
2020
10.1142/S0218001420520072
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Convolutional neural network, multi-dimensional long-short term memory network, histogram of oriented gradients, bidirectional long-short term memory network, hybrid language model, out of vocabulary word
Journal
34
Issue
ISSN
Citations 
12
0218-0014
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Sana Khamekhem Jemni111.72
Yousri Kessentini210015.39
Slim Kanoun320920.14