Abstract
In this paper, an efficient approach for text-independent writer identification using bag of words model and the combination of multiple classifiers is proposed. First of all, a bag of words model is established by extracting sub-images from the original handwriting image. Then, features are extracted by moment method, direction index histogram method and simplified Wigner method respectively to calculate the distance between the sub images having the same labels. Finally, the handwriting classification task is completed by means of feature fusion and multi-classifier combination. To evaluate this approach, writer identification is conducted on IAM English database. Experimental results revealed that the proposed writer identification algorithm with small number of characters and unconstrained contents achieves interesting results as compared to those reported by the existing writer recognition systems.
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Acknowledgment
This work is supported by University Scientific Research Program Natural Science Youth Project of Xinjiang Uyghur Autonomous Region (Grant No. XJUDU2019Y032), and the Tender Subject for Key Laboratory Project of Xinjiang Normal University (Grant No. XJNUSYS092018A02).
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Litifu, A., Yan, Y., Xiao, J., Jiang, H., Yao, W., Wang, J. (2020). Writer Identification Based on Combination of Bag of Words Model and Multiple Classifiers. In: Cree, M., Huang, F., Yuan, J., Yan, W. (eds) Pattern Recognition. ACPR 2019. Communications in Computer and Information Science, vol 1180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3651-9_6
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DOI: https://doi.org/10.1007/978-981-15-3651-9_6
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