Digitization of Bangla degraded document by Optical Character Recognition is a research activities now a days. Some historical documents particularly of 60s and 70s are degrading day by day due to lack of preservation. Those need to be retrieved. In this article, we present our recent study on recognition of degraded printed document images of Bangla, the 7th most popular language in the world. In the proposed approach the input will be low quality degraded images and the output is the recognized characters. In the first step some preprocessing are done on the document image to improve the quality of the scanned image. The proposed approach is an analytic approach. The segmentation is carried out line by line, word by word and finally character by character. The database used is the ISIDDI database. The total number of historical pages in TIF and JPG formats are 535, containing different fonts, sizes, formats and most importantly different levels of degradations. After segmentation we have manually identified 320 classes of such segmented symbols and divided the whole character dataset into test set (30%) and training set (70%). From the training set of 320 classes we have computed the Histogram of gradient feature or HOG feature on the samples. By applying the K-means clustering algorithm clusters for 320 classes has been generated and labeled according to the classes. For a character of test set again the HOG is computed and by applying k-nearest neighbour algorithm with the 320 classes the character is assigned to a character class with the minimum distance. The classification accuracy obtained on the test set is encouraging. We have achieved 82. 80% character or symbol level accuracy on 320 classes from the confusion matrix.

Degraded document recognition, Bangla document analysis, K-Means, k-nearest neighbour

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