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Cursive scene text analysis by deep convolutional linear pyramids
conference contribution
posted on 2018-01-01, 00:00 authored by S B Ahmed, S Naz, Imran RazzakImran Razzak, R Yusof© 2018, Springer Nature Switzerland AG. The camera captured images have various aspects to investigate. Generally, the emphasis of research depends on the interesting regions. Sometimes the focus could be on color segmentation, object detection or scene text analysis. The image analysis, visibility and layout analysis are the tasks easier for humans as suggested by behavioural trait of humans, but in contrast when these same tasks are supposed to perform by machines then it seems to be challenging. The learning machines always learn from the properties associated to provided samples. The numerous approaches are designed in recent years for scene text extraction and recognition and the efforts are underway to improve the accuracy. The convolutional approach provided reasonable results on non-cursive text analysis appeared in natural images. The work presented in this manuscript exploited the strength of linear pyramids by considering each pyramid as a feature of the provided sample. Each pyramid image process through various empirically selected kernels. The performance was investigated by considering Arabic text on each image pyramid of EASTR-42k dataset. The error rate of 0.17% was reported on Arabic scene text recognition.
History
Event
Neural Information Processing. International Conference (2018 : Siem Reap, Cambodia)Volume
11301Series
Lecture Notes in Computer SciencePagination
307 - 318Publisher
SpringerLocation
Siem Reap, CambodiaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-12-13End date
2018-12-16ISSN
0302-9743eISSN
1611-3349ISBN-13
9783030041663Language
engPublication classification
E1.1 Full written paper - refereedEditor/Contributor(s)
L Cheng, A Leung, S OzawaTitle of proceedings
ICONIP 2018 : International Conference on Neural Information ProcessingUsage metrics
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