Trends in Handwriting Recognition

The field of Handwriting recognition has evolved over the past three or four decades into a broad based activity which has had a measurable impact on applications. Some of the most significant practical impact has occurred in the past decade in handwriting recognition. Successful application of the established methods requires good understanding of their behavior and how well they match a particular context. Difficulties can arise from either the intrinsic complexity of a problem or a mismatch of methods to problems. Many emerging applications of involve complicated high-dimensional pattern spaces, small amounts of data-per-dimension, low signal-to-noise ratio, poorly specified statistical distributions, and anomalous statistical outliers. In some cases these difficulties are compounded by distributed data collection requirements that impose constraints on data integration and decentralized decision making. This creates both challenges and opportunities for Handwriting recognition research. This survey divides various approaches to handwriting recognition in nine different categories. Authors explore resent trends in Handwriting recognition and describe the areas of challenges and some possible solutions.