Custom OCR for Identity Documents:OCRXNet
Keywords:Text Detector, Tesseract, Yolo, CRAFT, Noise Removal.
Recent advancements in the area of Optical Character Recognition (OCR) using deep learning techniques made it possible to use for real world applications with good accuracy. In this paper we present a system named as OCRXNet. OCRXNetv1, OCRXNetv2 and OCRXNetv3 are proposed and compared on different identity documents. Image processing methods and various text detectors have been used to identify best fitted process for custom ocr of identity documents. We also introduced the end to end pipeline to implement OCR for various use cases.
 Brzeski, Adam, et al. "Evaluating performance and accuracy improvements for attention-OCR." IFIP International Conference on Computer Information Systems and Industrial Management. Springer, Cham, 2019.
 Saluja, Rohit, et al. "OCR On-the-Go: Robust End-to-end Systems for Reading License Plates & Street Signs." 15th IAPR International Conference on Document Analysis and Recognition (ICDAR). 2019.
 Achanta, Rakesh, and Trevor Hastie. "Telugu OCR framework using deep learning." arXiv preprint arXiv:1509.05962 (2015).
 Namysl, Marcin, and Iuliu Konya. "Efficient, lexicon-free OCR using deep learning." arXiv preprint arXiv:1906.01969 (2019).
 Wei, Tan Chiang, U. U. Sheikh, and Ab Al-Hadi Ab Rahman. "Improved optical character recognition with deep neural network." 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA). IEEE, 2018.
 Kundaikar, Teja, and Jyoti D. Pawar. "Multi-font Devanagari Text Recognition Using LSTM Neural Networks." First International Conference on Sustainable Technologies for Computational Intelligence. Springer, Singapore, 2020.
 Chandrakala, H. T., and G. Thippeswamy. "Deep Convolutional Neural Networks for Recognition of Historical Handwritten Kannada Characters." Frontiers in Intelligent Computing: Theory and Applications. Springer, Singapore, 2020. 69-77.
 Ali, Hazrat, et al. "Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network." SN Applied Sciences 2.2 (2020): 152.
 Springmann, Uwe, et al. "OCR of historical printings of Latin texts: problems, prospects, progress." Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage. 2014.
 Wang, Jianfeng, and Xiaolin Hu. "Gated recurrent convolution neural network for ocr." Advances in Neural Information Processing Systems. 2017.
 Ahmed, Saad Bin, et al. "Deep learning based isolated Arabic scene character recognition." 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR). IEEE, 2017.
 Naseer, Asma, and Kashif Zafar. "Meta features-based scale invariant OCR decision making using LSTM-RNN." Computational and Mathematical Organization Theory 25.2 (2019): 165-183.
 Maalej, Rania, and Monji Kherallah. "Improving MDLSTM for offline Arabic handwriting recognition using dropout at different positions." International conference on artificial neural networks. Springer, Cham, 2016.
 Breuel, Thomas M. "High performance text recognition using a hybrid convolutional-lstm implementation." 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Vol. 1. IEEE, 2017.
 Liu, Wei, et al. "Ssd: Single shot multibox detector." European conference on computer vision. Springer, Cham, 2016.
 Zhou, Xinyu, et al. "EAST: an efficient and accurate scene text detector." Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 2017.
 Baek, Youngmin, et al. "Character region awareness for text detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
 Kettunen, Kimmo, and Mika Koistinen. "Open Source Tesseract in Re-OCR of Finnish Fraktur from 19th and Early 20th Century Newspapers and Journals-Collected Notes on Quality Improvement." DHN. 2019.
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