Abstract
Recognition of handwritten characters and words is challenging due to the presence of complex character sets and the complexity of the words. The machine learning models with feature extraction methods will help us to solve the problem of recognizing handwritten words. The various preprocessing techniques applied to the word are Bilateral filters, resizing the images to find the Region of Interest (ROI) by contour detection and cropping the images. After resizing the image, it is further deskewed for better results. The recognition of handwritten Kannada words by extracting histogram of oriented gradients (HOG) features from the word image using various Machine Learning (ML) techniques are presented in this paper. Then the recognized word is converted to speech using the Google Text-to-Speech (gTTS) API. The dataset consists of 54,742 handwritten word images. Various machine learning models like Support Vector Machine (SVM), k-nearest neighbors (KNN), and random forest were applied to the dataset. Average accuracy of 88% is obtained using the SVM classifier with Radial Basis Function (RBF) kernel.
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References
Malakara, S., Dasa, R.K., Sarkarb, R., Basub, S., Nasipurib, M.: Handwritten and printed word identification using gray-scale feature vector and decision tree classifier. Procedia Technol. 10, 831–839 (2013). https://doi.org/10.1016/j.protcy.2013.12.428
Srimany, A., Chowdhuri, S.D., Bhattacharya, U., Parui, S.K.: Holistic recognition of online handwritten words based on an ensemble of svm classifiers. In: 11th IAPR International Workshop on Document Analysis Systems, pp. 86–90 (2014). https://doi.org/10.1109/DAS.2014.67
Nobile, N., Khayyat, M., Lam, L., Suen, C.Y.: Novel handwritten words and documents databases of five middle eastern languages. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 152–157 (2014)
Kumar, S.: A study for handwritten devanagari word recognition. In: 2016 International Conference on Communication and Signal Processing, pp. 1009–1014. (2016). https://doi.org/10.1109/ICCSP.2016.7754301.
Zhu, B., Shivram, A., Govindaraju, V., Nakagawa, M.: Online handwritten cursive word recognition by combining segmentation-free and segmentation-based methods. In: 15th International Conference on Frontiers in Handwriting Recognition, pp. 416–422 (2016)
Khemiri, A., Echi, A.K., Bela ̈ıd, A., Elloumi, M.: A system for off-line arabic handwritten word recognition based on bayesian approach. In: 15th International Conference on Frontiers in Handwriting Recognition 2016, pp. 560–565 (2016). https://doi.org/10.1109/ICFHR.2016.0108.
Aravinda, C.V, Prakash, H.N., Lavanya, S.: Kannada handwritten character recognition using multi feature extraction techniques. Int. J. Sci. Res. (IJSR) 3(10), 911–916 (2014)
Patel, M.S., Kumar, R.: Offline kannada handwritten word recognition using support vector machines (SVM). Int. J. Comput. Sci. Inf. Technol. Res. 936–942 (2015)
Ansari, S., Bhavani, S., Sutar, U.S.: Devanagari handwritten word recognition using efficient and fast feed forward neural network classifier. Int. J. Adv. Res. (IJAR) 2034–2042 (2017)
Sandyal, K.S., Patel, M.S.: Offline handwritten kannada word recognition. In: Proceedings of 07th IRF International Conference, pp. 19–22 (2014)
Ragha, L.R., Sasikumar, M.: Feature analysis for handwritten kannada kagunita recognition. Int. J. Comput. Theory Eng. 3(1), 94–102 (2011)
Acharya, A., Rakshit, S., Sarkar, R., Basu, S., Nasipuri, M.: Handwritten word recognition using MLP based classifier: a holistic approach. IJCSI Int. J. Comput. Sci. 422–526 (2013)
Thungamani, M., Ramakhanth Kumar, P., Prasanna, K., Rau, S.K.: Offline handwritten kannada text recognition using support vector machine using zernike moments. IJCSNS Int. J. Comput. Sci. Netw. Sec. 128–134 (2011)
Tulika Sureka, K.S.N., Swetha, I.A., Mamatha, H.R.: Word recognition techniques for kannada handwritten documents. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7. Kanpur, India (2019)
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Hebbi, C., Sooraj, J.S., Mamatha, H.R. (2022). Text to Speech Conversion of Handwritten Kannada Words Using Various Machine Learning Models. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_3
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DOI: https://doi.org/10.1007/978-981-16-6616-2_3
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