Abstract
Optical character recognition (OCR) is the electronic transformation of images into a computer-encoded text. OCR systems often produce poor accuracy for noisy images. Ensemble recognition techniques are used to improve OCR accuracy. The idea of the ensemble recognition techniques is to produce N-versions of an input image. These versions are similar but not identical. They are passed through the OCR engine to turn them into different OCR outputs, which later leads to select the best between them. Existing ensemble techniques need to be more effective to reduce OCR error rate. This research proposed enhanced ensemble technique to overcome the drawbacks of existing techniques. The proposed technique was evaluated against three other relevant existing techniques. The performance measurements used in this research were Word Error Rate (WER) and Character Error Rate (CER). Experimental results showed a relative decrease of 14.37% and 40.13% over the WER and CER of the best existing technique. This study contributes to the OCR domain as the proposed technique could facilitate the automatic recognition of documents. Hence, it will lead to a better information extraction.
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References
Habeeb, I.Q., Yusof, S.A., Ahmad, F.B.: Two bigrams based language model for auto correction of arabic OCR errors. Int. J. Digit. Content Technol. Appl. 8(1), 72–80 (2014)
Habeeb, I.Q.: Hybrid model of post-processing techniques for arabic optical character recognition. Universiti Utara Malaysia, Kedah, Malaysia (2016)
Ma, D., Agam, G.: A super resolution framework for low resolution document image OCR. In: Proceedings of the International Society for Optical Engineering (SPIE) on Document Recognition and Retrieval XX. International Society for Optics and Photonics, California, USA (2013)
Ma, D., Agam, G.: Lecture video segmentation and indexing. In: Proceedings of the International Society for Optical Engineering (SPIE) on Document Recognition and Retrieval XIX. International Society for Optics and Photonics, California, USA (2012)
Habeeb, I.Q., Yusof, S.A., Ahmad, F.B.: Improving optical character recognition process for low resolution images. Int. J. Adv. Comput. Technol. 6(3), 13–21 (2014)
Lund, W.B., Ringger, E.K., Walker, D.D.: How well does multiple OCR error correction generalize? In: Proceedings of Document Recognition and Retrieval XXI (DRR 2014). International Society for Optics and Photonics, San Francisco, USA (2014)
Herceg, P., et al.: Optimizing OCR accuracy for bi-tonal, noisy scans of degraded arabic documents. In: Proceedings of the International Society for Optical Engineering (SPIE) on Visual Information Processing. SPIE - The International Society for Optical Engineering, Florida, USA (2005)
Bassil, Y., Alwani, M.: OCR post-processing error correction algorithm using google online spelling suggestion. J. Emerg. Trends Comput. Inf. Sci. 3(1), 90–99 (2012)
Al-Zaydi, Z.Q., Vuksanovic, B., Habeeb, I.Q.: Image processing based ambient context-aware people detection and counting. Int. J. Mach. Learn. Comput. (IJMLC) 8(3), 268–273 (2018)
Lund, W.B., Walker, D.D., Ringger, E.K.: Progressive alignment and discriminative error correction for multiple OCR engines. In: Proceedings of the 11th International Conference on Document Analysis and Recognition (ICDAR 2011). IEEE, Beijing (2011)
Lund, W.B., Kennard, D.J., Ringger, E.K.: Why multiple document image binarizations improve OCR. In: Proceedings of the Workshop on Historical Document Imaging and Processing (HIP 2013). ACM, Washington (2013)
Al Azawi, M.: Statistical Language Modeling for Historical Documents Using Weighted Finite-State Transducers and Long Short-Term Memory. Technical University of Kaiserslautern, Kaiserslautern (2015)
Volk, M., Furrer, L., Sennrich, R.: Strategies for reducing and correcting OCR errors. In: Sporleder, C., van den Bosch, A., Zervanou, K. (eds.) Language Technology for Cultural Heritage. Theory and Applications of Natural Language Processing. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20227-8_1
Lund, W.B.: Ensemble Methods for Historical Machine-Printed Document Recognition. Brigham Young University, Utah (2014)
Al-Zaydi, Z.Q., Salam, H.: Multiple outputs techniques evaluation for arabic character recognition. Int. J. Comput. Tech. (IJCT) 2(5), 1–7 (2015)
Batawi, Y., Abulnaja, O.: Accuracy evaluation of arabic optical character recognition voting technique: experimental study. Int. J. Electr. Comput. Sci. 12(1), 29–33 (2012)
Lund, W.B., Ringger, E.K.: Error correction with in-domain training across multiple OCR system outputs. In: Proceedings of the 11th International Conference on Document Analysis and Recognition (ICDAR 2011). IEEE, Beijing (2011)
Lund, W.B., Ringger, E.K.: Improving optical character recognition through efficient multiple system alignment. In: Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries. ACM, Austin (2009)
Kittler, J., et al.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)
Lopresti, D., Zhou, J.: Using consensus sequence voting to correct OCR errors. Comput. Vis. Image Underst. 67(1), 39–47 (1997)
Lund, W.B., Kennard, D.J., Ringger, E.K.: Combining multiple thresholding binarization values to improve OCR output. In: Proceedings of the International Society for Optical Engineering (SPIE) on Document Recognition and Retrieval XX. SPIE - The International Society for Optical Engineering. San Francisco, California (2013)
Abdulkhudhur, H.N., et al.: Implementation of improved Levenshtein algorithm for spelling correction word candidate list generation. J. Theor. Appl. Inf. Technol. 88(3), 449–455 (2016)
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Habeeb, I.Q., Al-Zaydi, Z.Q., Abdulkhudhur, H.N. (2018). Enhanced Ensemble Technique for Optical Character Recognition. In: Al-mamory, S., Alwan, J., Hussein, A. (eds) New Trends in Information and Communications Technology Applications. NTICT 2018. Communications in Computer and Information Science, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-030-01653-1_13
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