Abstract
This paper presents a method to retrieve words from Kannada documents. It works on Histogram of Oriented Gradients (HOG) and Morphological filters. A large dataset of 50000 words is created using 250 document pages belongs to different categories. A preprocessed document image is segmented using simple morphological filters. The histogram channels are designed over four-sided cells (i.e. R-HOG) to compute gradients of a word image. In parallel, morphological erosion, opening, top and bottom hat transformations are applied on each word. The densities of the resultant images are estimated. Later on, HOG and morphological features are fused. Then, the cosine distance is used to measure the similarity between two words i.e., query and candidate word, based on it, the relevance of the word is estimated by generating distance ranks. Then correctly matched words are selected at threshold 98%. The experimental results confirm the efficiency of our proposed method in terms of the average precision rate 91.23%, and average recall rate 84.78% as well as average F-measure 89.47%.
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Hangarge, M., Veershetty, C., Rajmohan, P., Mukarambi, G. (2017). Word Retrieval from Kannada Document Images Using HOG and Morphological Features. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_7
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