Age-Type Identification and Classification of Historical Kannada Handwritten Scripts using Line Segmentation with HOG feature Descriptors
Languages of publication
The offline handwritten text recognition is one of the most challenging tasks in document image analysis; our aim is to recreate the cultural importance of the Kannada Language writing tradition through the historical degraded manuscripts. In the present digital era, we need to protect and digitize the resources of our Indian culture and heritage by digitizing those manuscripts which are losing its status; the degraded manuscripts are influenced by weather condition. In this paper, we have attempted to identify and recognise the historical Kannada handwritten scripts of various dynasties; namely, Vijayanagara dynasty (1460 AD), Mysore Wadiyar dynasty (1936 AD), Vijayanagara dynasty (1400 AD) and Hoysala dynasty (1340 AD) by using the improved seam carving text line segmentation method with HOG feature descriptors. The average classification accuracy for different dynasties are computed. The LDA classifier is yielded 93.4%, K-NN classifier has yielded 92% and SVM classifier has 95.5%. Based on the experimentation, the SVM classifier has recorded good classification performance comparatively LDA and K-NN classifiers for historical Kannada handwritten scripts.
-  Nikolaos Arvanitopoulos and Sabine Susstrunk, Seam Carving for Text Line Extraction on Color and Grayscale Historical Manuscripts. 14th IEEE International Conference on Frontiers in Handwriting Recognition, pp.726-731. DOI: 10.1109/ICFHR.2014.127
-  S. Avidan, A. Shamir, Seam Carving for Content-Aware Image Resizing, ACM Transactions on Graphics, vol. 26, no. 3, p. 10, 2007.
-  Veronica Romero, Joan Andreu Sanchez, Vicente Bosch, Katrien Depuydt and Jesse de Does, Influence of Text Line Segmentation in Handwritten Text Recognition. 13th IEEE International Conference on Document Analysis and Recognition (ICDAR), 2015, 978-1-4799-1805-8/15, pp. 536-540
-  Mohamed Elleuch, Ansar Hani and Monji Kherallah. Arabic Handwritten Script Recognition System Based on HOG and Gabor Features. The International Arab Journal of Information Technology, Vol. 14, No. 4A, Special Issue 2017, pp. 639-646
-  Y. Elfakir, G. Khaissidi, M. Mrabti and D. Chenouni, Handwritten Arabic Documents Indexation using HOG Feature. International Journal of Computer Applications (0975 – 8887) Volume 126, No. 9, September 2015, pp. 14-18.
-  Taraggy M. Ghanim, Mahmoud I. Khalil, and Hazem M. Abbas, PHoG Features and Kullback-Leibler Divergence Based Ranking Method for Handwriting Recognition. ANNPR 2018, LNAI 11081, pp. 293–305, 2018. DOI:10.1007/978-3-319-99978-4_23
-  Joaquim Arlandis and Juan-Carlos Perez-Cortes, ”Fast Handwritten Recognition Using Continuous Distance Transformation. CIARP 2003, LNCS 2905, pp. 400–407, Springer-Verlag Berlin Heidelberg 2003.
-  Laurence Likforman-Sulem, Abderrazak Zahour and Bruno Taconet, Text line segmentation of historical documents: a survey. IJDAR (2007) 9: pp. 123-138, DOI 10.1007/s10032-006-0023-z
-  Abedelkadir Asi, Raid Saabni and Jihad El-Sana. Text Line Segmentation for Gray Scale Historical Document Images. HIP '11 Proceedings of the 2011 Workshop on Historical Document Imaging and Processing, pp. 120-126, 2011. DOI: 10.1145/2037342.2037362 pp. 120-126, 2011.
-  Parashuram Bannigidad and Chandrashekar Gudada, Restoration of Degraded Historical Kannada Handwritten Document Images using Image Enhancement Techniques. International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016), 2016. pp. 498-508
-  Parashuram Bannigidad and Chandrashekar Gudada, Restoration of Degraded Kannada Handwritten Paper Inscriptions (Hastaprati) using Image Enhancement Techniques. IEEE International Conference on Computer Communication and Informatics (ICCCI -2017), 2017. pp. 1-6.
-  Parashuram Bannigidad and Chandrashekar Gudada, Age-Type Identification and Recognition of Historical Kannada Handwritten Document Images Using HOG Feature Descriptors. Computing, Communication and Signal Processing, Advances in Intelligent Systems and Computing 810, 2019, pp. 1001-1010. DOI: 10.1007/978-981-13-1513-8_101
-  Navneet Dalal and Bill Triggs, Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp. 1-8
Publication order reference