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- W4387571251 abstract "Handwriting recognition is an active research in the field of pattern recognition, image processing and computer vision. The handwriting recognition system can recognize handwritten words extracted from the scanned document image. For a better recognition model, the most inevitable thing is a good dataset for training purpose. Here, the clustering is performed on the segmented handwritten words to group the matching words for easing the process of labelled dataset creation. In this, after doing some pre-processing operations, as a feature descriptor, histogram of oriented gradient features is extracted holistically on the word image. For feature reduction, Principal Component Analysis is applied on the extracted feature set and finally K-means clustering used for grouping the matched handwritten words together from the unlabelled dataset. Here, training the recognition model for keyword searching, the clustering helps to create a lexicon reduced dataset and the non-repeated segmented words are neglected after clustering. This will make the most complicated and time-consuming manual task, grouping and labelling the handwritten words for dataset creation easier." @default.
- W4387571251 created "2023-10-13" @default.
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- W4387571251 date "2023-01-01" @default.
- W4387571251 modified "2023-10-13" @default.
- W4387571251 title "HOG Feature-Based Offline Handwritten Malayalam Word Clustering with Lexicon Reduction" @default.
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- W4387571251 doi "https://doi.org/10.1007/978-981-99-2602-2_46" @default.
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