Symmetry features for license plate classification

Achieving high recognition rate for license plate images is challenging due to multi-type images. We present new symmetry features based on stroke width for classifying each input license image as private, taxi, cursive text, when they expand the symbols by writing and non-text such that an appropriate optical character recognition （OCR） can be chosen for enhancing recognition performance. The proposed method explores gradient vector flow （GVF） for defining symmetry features, namely, GVF opposite direction, stroke width distance, and stroke pixel direction. Stroke pixels in Canny and Sobel which satisfy the above symmetry features are called local candidate stroke pixels. Common stroke pixels of the local candidate stroke pixels are considered as the global candidate stroke pixels. Spatial distribution of stroke pixels in local and global symmetry are explored by generating a weighted proximity matrix to extract statistical features, namely, mean, standard deviation, median and standard deviation with respect the median. The feature matrix is finally fed to an support vector machine （SVM） classifier for classification. Experimental results on large datasets for classification show that the proposed method outperforms the existing methods. The usefulness and effectiveness of the proposed classification is demonstrated by conducting recognition experiments before and after classification.

Graphology based handwritten character analysis for human behaviour identification

Graphology-based handwriting analysis to identify human behavior,irrespective of applications,is interesting.Unlike existing methods that use characters,words and sentences for behavioural analysis with human intervention,we propose an automatic method by analysing a few handwritten English lowercase characters from a to z to identify person behaviours.The proposed method extracts structural features,such as loops,slants,cursive,straight lines,stroke thickness,contour shapes,aspect ratio and other geometrical properties,from different zones of isolated character images to derive the hypothesis based on a dictionary of Graphological rules.The derived hypothesis has the ability to categorise the personal,positive,and negative social aspects of an individual.To evaluate the proposed method,an automatic system is developed which accepts characters from a to z written by different individuals across different genders and age groups.This automatic privacy projected system is available on the website(http://subha.pythonanywhere.com).For quantitative evaluation of the proposed method,several people are requested to use the system to check their characteristics with the system automatic response based on his/her handwriting by choosing to agree or disagree options.The automatic system receives 5300 responses from the users,for which,the proposed method achieves 86.70%accuracy.

Channel-wise attention model-based fire and rating level detection in video

Due to natural disaster and global warning, one can expect unexpected fire, which causes panic among people and extent to death. To reduce the impact of fire, the authors propose a new method for predicting and rating fire in video through deep-learning models in this work such that rescue team can save lives of people. The proposed method explores a hybrid deep convolutional neural network, which involves motion detection and maximally stable extremal region for detecting and rating fire in video. Further, the authors propose to use a channel-wise attention mechanism of the deep neural network for detecting rating of fire level. Experimental results on a large dataset show the proposed method outperforms the existing methods for detecting and rating fire in video.