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Papers/Pixel-level Reconstruction and Classification for Noisy Ha...

Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters

Manohar Karki, Qun Liu, Robert DiBiano, Saikat Basu, Supratik Mukhopadhyay

2018-06-21Image ClassificationDocument Image ClassificationGeneral ClassificationClassification
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Abstract

Classification techniques for images of handwritten characters are susceptible to noise. Quadtrees can be an efficient representation for learning from sparse features. In this paper, we improve the effectiveness of probabilistic quadtrees by using a pixel level classifier to extract the character pixels and remove noise from handwritten character images. The pixel level denoiser (a deep belief network) uses the map responses obtained from a pretrained CNN as features for reconstructing the characters eliminating noise. We experimentally demonstrate the effectiveness of our approach by reconstructing and classifying a noisy version of handwritten Bangla Numeral and Basic Character datasets.

Results

TaskDatasetMetricValueModel
Document Image ClassificationNoisy Bangla CharactersAccuracy77.22Pixel-level RC
Document Image ClassificationNoisy Bangla NumeralAccuracy95.46Pixel-level RC
Document Image Classificationn-MNISTAccuracy97.62Pixel-level RC
Image ClassificationNoisy MNIST (Motion)Accuracy97.2Pixel-level RC
Image ClassificationNoisy MNIST (Contrast)Accuracy95.04Pixel-level RC
Image ClassificationNoisy MNIST (AWGN)Accuracy97.62Pixel-level RC
Image ClassificationNoisy Bangla CharactersAccuracy77.22Pixel-level RC
Image ClassificationNoisy Bangla NumeralAccuracy95.46Pixel-level RC
Image Classificationn-MNISTAccuracy97.62Pixel-level RC

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