Abstract
Extraction of text characters and strings from the nameplates, sign boards, printed names on bank cheques, printed postal envelope addresses and office boards can give useful information for numerous applications. The process to extract text characters from all the categories mentioned above is very complex task due to divergence in the text patterns in terms of text fonts used. Text recognition always starts with text detection. Therefore, text recognition is the process of text detection followed by text recognition. This can be done by combining image processing and machine learning together. This paper proposes a method for text detection and identification mainly on nameplates in the offices and home doors written in English language which uses Roman script. Initial step of text detection of Roman characters is carried out by using image processing and K-means clustering algorithm while later part of identification is carried out with the help of multiclass feature of support vector machine.
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Dhore, M., Varpe, K. (2021). Text Recognition Using K-means Clustering and Support Vector Machine. In: Patil, V.H., Dey, N., N. Mahalle, P., Shafi Pathan, M., Kimbahune, V.V. (eds) Proceeding of First Doctoral Symposium on Natural Computing Research. Lecture Notes in Networks and Systems, vol 169. Springer, Singapore. https://doi.org/10.1007/978-981-33-4073-2_10
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DOI: https://doi.org/10.1007/978-981-33-4073-2_10
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