Optical Character Recognition

Character Recognition (CR) has been an active area of research and due to its diverse applicable environment; it continues to be a challenging research topic. In this paper, we focus specially on off-line recognition of handwritten English words. The main approaches for off-line cursive word recognition can be divided into segmentation-based and holistic one. The holistic approach is used in recognition of limited size vocabulary where global features, extracted from the entire word image are considered. As the size of the vocabulary increases, the complexity of algorithms also increases linearly due to the need for a larger search space and a more complex pattern representation. Additionally, the recognition rates decrease rapidly due to the decrease in interclass variances in the feature space. The segmentation based strategies, on the other hand, employ bottom-up approaches, starting from stroke or character level and going towards producing a meaningful text. With the cooperation of segmentation stage, the problem is reduced to the recognition of simple isolated characters or strokes, which can be handled for unlimited vocabulary