Comparative Analysis of Raw Images and Meta Feature based Urdu OCR using CNN and LSTM
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 1
Abstract
Urdu language uses cursive script which results in connected characters constituting ligatures. For identifying characters within ligatures of different scales (font sizes), Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) Network are used. Both network models are trained on formerly extracted ligature thickness graphs, from which models extract Meta features. These thickness graphs provide consistent information across different font sizes. LSTM and CNN are also trained on raw images to compare performance on both forms of inputs. For this research, two corpora, i.e. Urdu Printed Text Images (UPTI) and Centre for Language Engineering (CLE) Text Images are used. Overall performance of networks ranges between 90% and 99.8%. Average accuracy on Meta features is 98.08% while using raw images, 97.07% average accuracy is achieved.
Authors and Affiliations
Asma Naseer, Kashif Zafar
Keywords
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- EP ID EP261663
- DOI 10.14569/IJACSA.2018.090157
- Views 74
- Downloads 0
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