Study of using hybrid deep neural networks in character extraction from images containing text
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Abstract
Character segmentation from epigraphical images helps the optical character recognizer (OCR) in training and recognition of old regional scripts. The scripts or characters present in the images are illegible and may have complex and noisy background texture. In this paper, we present an automated way of segmenting and extracting characters on digitized inscriptions. To achieve this, machine learning models are employed to discern between correctly segmented characters and partially segmented ones. The proposed method first recursively crops the document by sliding a window across the image from top to bottom to extract the content within the window. This results in a number of small images for classification. The segments are classified into character and non-character class based on the features within them. The model was tested on a wide range of input images having irregular, inconsistently spaced, hand written and inscribed characters.
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Manigandan T, Vidhya V, Dhanalakshmi V, Nirmala B (2017) Tamil character recognition from ancient epigraphical inscription using OCR and NLP. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, 1008-1011. Link: https://bit.ly/3jBsXkT
Likforman-Sulem L, Zahour A, Taconet B (2007) Text line segmentation of historical documents: a survey. International Journal of Document Analysis and Recognition (IJDAR) 9: 123-138. Link: https://bit.ly/3ik02SQ
Padmaprabha P, Ramappa MH (2018) A Systematic Approach in Transforming Inscriptions into Modern Text–Review. International Journal of Signal Processing, Image Processing and Pattern Recognition 11: 37-44. Link: https://bit.ly/3CdeC6m
Louloudis G, Gatos B, Pratikakis I, Halatsis K (2006) A block-based Hough transform mapping for text line detection in handwritten documents. In Tenth International Workshop on Frontiers in Handwriting Recognition. Suvisoft. Link: https://bit.ly/3jBoHlp
Kavitha AS, Shivakumara P, Kumar GH, Lu T (2016) Text segmentation in degraded historical document images. Egyptian Informatics Journal 17: 189-197. Link: https://bit.ly/3lvhIwz
Likforman-Sulem L, Zahour A, Taconet B (2006) Text Line Segmentation of Historical Documents: a Survey. International Journal on Document Analysis and Recognition. Link: https://bit.ly/3A32LG4
Sapirstein P (2019) Segmentation, Reconstruction, and Visualization of Ancient Inscriptions in 2.5D. Journal on Computing and Cultural Heritage (JOCCH 12: 1-30. Link: https://bit.ly/3ChPOKO
Murthy SK, Kumar HG, Shivakumar P, Ranganath PR (2004) nearest neighboring clustering based approach for line and character segmentation in epigraphical scripts. Link: https://bit.ly/2Vo3iE3
Bhat SS, Balachandra Achar HV (2016) Character recognition and Period prediction of ancient Kannada Epigraphical scripts. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering 3: 114-120. Link: https://bit.ly/3CeSKrd
Hu R, Odobez JM, Gatica-Perez D (2017) Extracting maya glyphs from degraded ancient documents via image segmentation. ACM Journal of Computational. Cultural. Heritage 10: 71-93. Link: https://bit.ly/3A8ZrcF
Mohana HS, Navya K, Rajithkumar BK, Nagesh C (2014) Interactive segmentation for character extraction in stone inscriptions. Second International Conference on Current Trends In Engineering and Technology - ICCTET. Link: https://bit.ly/3AmmSzx
Alberti M, Seuret M, Pondenkandath V, Ingold R, Liwicki M (2017) Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks. In Proceedings of 4th International Workshop on Historical Document Imaging and Processing, Kyoto, Japan. Link: https://bit.ly/2TTjEnr
Chen K, Liu C, Seuret M, Liwicki M, Hennebert J, et al. (2016) Page Segmentation for Historical Document Images Based on Superpixel Classification with Unsupervised Feature Learning. 2016 12th IAPR Workshop on Document Analysis Systems (DAS), Santorini 299-304. Link: https://bit.ly/2Vx1Gaz
Badjatiya P, Kurisinkel LJ, Gupta M, Varma V (2018) Attention-based Neural Text Segmentation. European Conference on Information Retrieval 2018, (ECIR-2018),Grenoble, France.
Jo J, Koo H, Soh JW, Cho N (2019) Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural Network. Link: https://bit.ly/37fU2nT
Al-Rawi M, Bazazian D, Valveny E (2019) Can Generative Adversarial Networks Teach Themselves Text Segmentation?. Link: https://bit.ly/3imtlUH
Reza MM, Bukhari SS, Jenckel M, Dengel A (2019) Table Localization and Segmentation using GAN and CNN. 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), Sydney, Australia 152-157. Link: https://bit.ly/3rQhe5D
Li X, Zhang X, Yang B, Xia S (2017) Character segmentation in text line via convolutional neural network. 2017 4th International Conference on Systems and Informatics (ICSAI), Hangzhou 1175-1180. Link: https://bit.ly/3xiIxX7
Zirari F, Ennaji A, Nicolas S, Mammass D (2013) A Document Image Segmentation System Using Analysis of Connected Components," 2013 12th International Conference on Document Analysis and Recognition, Washington, DC 753-757. Link: https://bit.ly/3A59U8Y
Manigandan T, Vidhya V, Dhanalakshmi V, Nirmala B (2017) Tamil character recognition from ancient epigraphical inscription using OCR and NLP. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, 1008-1011. Link: https://bit.ly/3jjfnlY
Sowmya A, Kumar H (2015) Enhancemnet and segmentation of historical records. Fifth intenation conference on CS and IT. Link: https://bit.ly/37iELCH
Abtahi F, Zhu Z, Burry AM (2015) A deep reinforcement learning approach to character segmentation of license plate images. 2015 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo 539-542. Link: https://bit.ly/3ftioyY
Bailey DG, Klaiber MG (2019) Zig-Zag Based Single-Pass Connected Components Analysis. J Imaging 5: 1-26. Link: https://bit.ly/3CfUeSn
Shrruru K (2020) An Introduction to Artificial Neural Network. International Journal Of Advance Research And Innovative Ideas In Education 1: 27-30.
Khan S, Rahmani H, Ali Shah SA, Bennamoun M, Medioni G, et al. (2018) A Guide to Convolutional Neural Networks for Computer Vision," in A Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool 1-18. Link: https://bit.ly/3lvdViP
Lipo W (2005) Support Vector Machines: Theory and Applications. Springer Science & Business Media 431. Link: https://bit.ly/3rZl38v
Ni KS, Nguyen TQ (2009) An Adaptable k-Nearest Neighbors Algorithm for MMSE Image Interpolation. IEEE Transactions on Image Processing 18: 1976-1987. Link: https://bit.ly/3xmPh6o
Lin T (2018) PCA/SVM-Based Method for Pattern Detection in a Multisensor System. Mathematical Problems Engineering, Hindawi. 1-11. Link: https://bit.ly/3yt8bKg