The process of recognizing scanned documents or machine printed documents using automated or semi-automated tools are resulted into wide range of applications in different real life domains. There are different techniques already introduced by various authors for efficient and accurate recognition of handwritten characters. As designing a method with 100 % accuracy of character recognition is challenging and unachievable task for researchers due to presence of noise, distinct styles of font under real time environment, therefore it is required to design recognition method by considering these characteristics of character recognition. This paper presenting online handwritten recognition framework by using efficient hybrid features codebook and Feed forward neural network (FFNN) to improve the recognition accuracy over Devanagari scripts. Along with the accuracy, another term which plays vital role of deciding the efficiency of recognition method is time required for recognition. Previous techniques giving the more accuracy for recognition, however feature extraction process takes longer time. Therefore such methods failed in real time applications. This paper majorly focusing on different recognition methods previously used and there recognition results, and then presenting our recognition method with its practical results for analysis. The results are varying by considering different image size in MATLAB.

In this paper, online handwritten Devnagari word recognition system is proposed and discussed. The increase in usage of handheld devices which accept handwritten data as input created a demand for application which analyze and recognize data efficiently. Due to the popularity of digital device, we use Smartphone as input device. Input image is drawn on Smartphone. Feature extraction of input image is done by android technology. Using that features HMM recognizes the word. Experimental results show advantages of this method in the field of handwriting recognition.

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Today world the brain tumor is life threatening and the main reason for the death. The growth of abnormal cells in brain leads to brain tumor. Brain tumor is categorized into malignant tumor and benign tumor. Malignant is cancerous whereas Benign tumor is non-cancerous. Diagnosing at earlier stage can save the person. It is actually a great challenge to find the brain tumor and classifying its type. Detection of Brain Tumor and the correct analysis of the Tumor structure is difficult task. To overcome the drawbacks of exiting brain tumor detection methods the proposed system is presented using KNN & LLOYED clustering. Undoubtedly, this saves the time as well as it gives more accurate results as in comparison to manual detection. The proposed method is a novel approach for detection Tumor along with the ability to calculate the area (%age) occupied by the Tumor in the overall brain cells. Firstly, Tumor regions from an MR image are segmented using an OSTU Algorithm. KNN& LLOYED are used for detecting as well as distinguishing Tumor affected tissues from the not affected tissues. Total twelve features are extracted like correlation, contrast, energy, homogeneity etc. by performing "wavelet transform on the converted gray scale image". For feature extraction DB5 wavelet transform is used.