Paper Info
Title
Arabic Video Text Recognition Based On Multi-Dimensional Recurrent Neural Networks
Abstract
In this paper we propose a novel method based on Recurrent Neural Networks (RNN) for text recognition in Arabic news video frames. In fact, embedded texts in videos represent a rich source of information for indexing and automatic processing of multimedia documents. However, text recognition in video is not trivial due to many challenges like background complexity (e.g., presence of text-like objects), unknown text size/font with various colours and degraded text quality. The proposed system presents a segmentation-free recognition technique (i.e. no prior required segmentation of words into characters) using a RNN architecture. This technique relies specifically on a Multi-Dimensional Long Short Term Memory (MDLSTM) with a Connectionist Temporal Classification (CTC) output layer. Our system has been evaluated on the public AcTiV-R dataset. The obtained results are very promising.
Year
DOI
Venue
2017
10.1109/AICCSA.2017.126
2017 IEEE/ACS 14TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA)
Keywords
Field
DocType
Arabic video text recognition, RNN, MDLSTM, CTC, AcTiV-R database
Arabic,Computer science,Segmentation,Font,Search engine indexing,Recurrent neural network,Image segmentation,Speech recognition,Real-time computing,Connectionism,Text recognition
Conference
ISSN
Citations 
PageRank 
2161-5322
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Oussama Zayene172.82
Soumaya Essefi Amamou200.34
Najoua Essoukri Ben Amara320941.48