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Neural Networks Pipeline for Offline Machine Printed Arabic OCR

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Abstract

In the context of Arabic optical characters recognition, Arabic poses more challenges because of its cursive nature. We purpose a system for recognizing a document containing Arabic text, using a pipeline of three neural networks. The first network model predicts the font size of an Arabic word, then the word is normalized to an 18pt font size that will be used to train the next two models. The second model is used to segment a word into characters. The problem of words segmentation in the Arabic language, as in many similar cursive languages, presents a challenge to the OCR systems. This paper presents a multichannel neural network to solve the offline segmentation of machine-printed Arabic documents. The segmented characters are then fed as an input to a convolutional neural network for Arabic characters recognition. The font size prediction model produced a test accuracy of 99.1%. The accuracy of the segmentation model using one font is 98.9%, while four-font model showed 95.5% accuracy. The whole pipeline showed an accuracy of 94.38% on Arabic Transparent font of size 18pt from APTI data set.

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Notes

  1. Keras was used for experiments https://github.com/fchollet/keras/.

  2. Data used from https://sites.google.com/site/motazsite/.

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Authors and Affiliations

  1. Computers and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt

    Mohamed A. Radwan, Mahmoud I. Khalil & Hazem M. Abbas

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  1. Mohamed A. Radwan
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  2. Mahmoud I. Khalil
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  3. Hazem M. Abbas
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Correspondence to Mahmoud I. Khalil.

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Radwan, M.A., Khalil, M.I. & Abbas, H.M. Neural Networks Pipeline for Offline Machine Printed Arabic OCR. Neural Process Lett 48, 769–787 (2018). https://doi.org/10.1007/s11063-017-9727-y

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  • DOI: https://doi.org/10.1007/s11063-017-9727-y

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