This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.
The Leukocytes are differentiated from each other on the basis of their nuclei, demanded in many Medical studies, especially in all types of Leukemia by the Hematologists to note the disorder caused by specific type of Leukocyte. Leukemia is a life threatening disease. The work for diagnosing is manually carried out by the Hematologists involving much labor, time and human errors. The problems mentioned are easily addressed through computer vision techniques, but still accuracy and efficiency are demanded in terms of the basic and challenging step segmentation of Leukocyte's nuclei. The underlying study proposed better method in terms of accuracy and efficiency by designing a dynamic convolution filter for boosting low intensity values in the separated green channel of an RGB image and suppressing the high values in the same channel. The high values in the green channel become 255 (background) while the nuclei always have low values in the green channel and thus clearly appear as foreground. The proposed technique is tested on 365 images achieving an overall accuracy of 95.89%, while improving the efficiency by 10%. The proposed technique achieved its targets in a realistic way by improving the accuracy as well as the efficiency and both are highly required in the area.