Feature plays a very important role in the area of image processing. Before getting features, various image preprocessing techniques like binarization, thresholding, resizing, normalization etc. are applied on the sampled image. After that, feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. Feature extraction techniques are helpful in various image processing applications e.g. character recognition. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. Here in this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique, will be better. Hereby in this paper, we are going to refer features and feature extraction methods in case of character recognition application.

A Detailed Review of Feature Extraction in Image Processing Systems

Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features

Fuzzy model based recognition of handwritten numerals

This paper describes a neural network-based technique for cursive character recognition applicable to segmentation-based word recognition systems. The proposed research builds on a novel feature extraction technique that extracts direction information from the structure of character contours. This principal is extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image. The proposed technique is compared with the standard direction feature extraction technique, providing promising results using segmented characters from the CEDAR benchmark database.

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A modified direction feature for cursive character recognition

Development of an automated system for identifying and classifying different diseases of the contaminated plants is an emerging research area in precision agriculture. Identification of the diseases is the key to prevent qualitative and quantitative loss of agricultural yields. Rice (Oryza Sative) is one of the essential crops in India and losses due to the diseases badly impact the economy. Manual detection of the diseases is very difficult and not accurate. This creates a need for Image processing techniques which will help in accurate and timely detection of the diseases and overcome the limitations of the human vision. A new technique to diagnose and classify rice diseases has been proposed in this paper. Four diseases namely rice bacterial blight, rice blast, rice brown spot and rice sheath rot have been identified and classified. Different features like shape, the color of a diseased portion of the leaf have been extracted by developing an algorithm. All the extracted features have been combined as per the diseases and diseases have been classified using Minimum Distance Classifier (MDC) and k-Nearest Neighbor classifier (k-NN). The performance of the proposed technique has been evaluated with the help of 115 rice leaf images of four diseases and 70 percent image data has been used for training the classifier and 30 percent has been used for testing. Classification accuracy has been calculated for each disease using both classifiers. The overall accuracy achieved by using k-NN and MDC is 87.02 percent and 89.23 percent respectively.

Monitoring and controlling rice diseases using Image processing techniques

High accuracy character recognition techniques can provide useful information for segmentation-based handwritten word recognition systems. This research describes neural network-based techniques for segmented character recognition that may be applied to the segmentation and recognition components of an off-line handwritten word recognition system. Two neural architectures along with two different feature extraction techniques were investigated. A novel technique for character feature extraction is discussed and compared with others in the literature. Recognition results above 80% are reported using characters automatically segmented from the CEDAR benchmark database as well as standard CEDAR alphanumerics.

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H. Basli

Papers

A novel feature extraction technique for the recognition of segmented handwritten characters