Reconstruction filters in computer-graphics
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
A New Arabic Printed Text Image Database and Evaluation Protocols
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Multi-dimensional recurrent neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
An Evaluation of HMM-Based Techniques for the Recognition of Screen Rendered Text
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
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OCR of multi-font Arabic text is difficult due to large variations in character shapes from one font to another. It becomes even more challenging if the text is rendered at very low resolution. This paper describes a multi-font, low resolution, and open vocabulary OCR system based on a multidimensional recurrent neural network architecture. For this work, we have developed various systems, trained for single-font/single-size, single-font/multi-size, and multi-font/multi-size data of the well known Arabic printed text image database (APTI). The evaluation tasks from the second Arabic text recognition competition, organized in conjunction with ICDAR 2013, have been adopted. Ten Arabic fonts in six font size categories are used for evaluation. Results show that the proposed method performs very well on the task of printed Arabic text recognition even for very low resolution and small font size images. Overall, the system yields above 99% recognition accuracy at character and word level for most of the printed Arabic fonts.