A 3D Approach for Palm Leaf Character Recognition Using Histogram Computation and Distance Profile Features
Handwritten character recognition has been a well-known area of research for last five decades. This is an important application of pattern recognition in image processing. Generally 2D scanning is used and the text is captured in the form of an image. In
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Abstract Handwritten character recognition has been a well-known area of research for last five decades. This is an important application of pattern recognition in image processing. Generally 2D scanning is used and the text is captured in the form of an image. In this work instead of regular scanning method, the X, Y co-ordinates are measured using measuroscope at every pixel point. Further a 3D feature, depth of indentation, ‘Z’, which is proportional to the pressure applied by the scriber at that point, is measured using a dial gauge indicator. In the present work the profile based features extracted for palm leaf character recognition are ‘histogram’ and ‘distance’ profiles. The recognition accuracy obtained using the Z-dimension, a 3D feature, is very high and the best result obtained is 92.8 % using histogram profile algorithm. Keywords Palm leaf character recognition ⋅ Histogram profile ⋅ Distance profile ⋅ k-NN classifier ⋅ 3D feature
1 Introduction Technological developments gave the printed character recognition a new dimension called Optical Character Recognition (OCR). In the initial stages, most of the work was contributed towards the printed English characters, due to more number P.N. Sastry (✉) ⋅ N.V. Koteswara Rao CBIT, Hyderabad, India e-mail: [email protected] N.V. Koteswara Rao e-mail: [email protected] T.R. Vijaya Lakshmi MGIT, Hyderabad, India e-mail: [email protected] K. RamaKrishnan ADRIN, Indian Institute of Space Science and Technology, Trivandrum, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2017 S.C. Satapathy et al. (eds.), Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, Advances in Intelligent Systems and Computing 516, DOI 10.1007/978-981-10-3156-4_40
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of English speakers compared to any of the Indian languages. However, handwritten character recognition became important in due course of time for the application of automatic mail sorting (zip code identification). Further, signature verification of forensic departments and authenticity of a document written by a specific scriber increased the importance of handwritten character recognition.
2 Previous Work Sastry and Krishnan developed database and test characters for Palm Leaf Character Recognition (PLCR) pertaining to Telugu (a south Indian language) [1–5]. They developed many models using 2D correlation, PCA and Radon transform [1–3] in the area of Palm Leaf Character Recognition. Based on the measure of similarity all the Telugu characters were divided into 3 Co-ordinate planes which are XZ, XY and YZ. The best recognition accuracy was reported in the YZ plane for all the methods and found to be 90 %. Patvardhan et al. [6, 7] presented a denoising approach using discrete curve-let transform and binarization technique using wavelets. Manjunath Aradhya and others [8] proposed character identification using combination of FT and PCA. The documents were scanned on a HP 2400 scan jet scanner and subsequently
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