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Offline cursive script recognition and their associated issues are still fresh despite of last few decades' research. This paper presents an annotated comparison of proposed and recently published preprocessing techniques with reported work in the offline cursive script recognition. Normally, in the offline script analysis, the input is a paper image or a word or a digit and the desired output is ASCII text. This task involves several preprocessing steps, and some of them are quite hard such as line removal from text, skew removal, reference line detection (lower/upper baselines), slant removal, scaling, noise elimination, contour smoothing and skeleton. Moreover, subsequent stage of segmentation (if any) and recognition is also highly dependent on these preprocessing techniques. This paper presents an analysis and annotated comparison of latest preprocessing techniques proposed by authors with those reported in the literature on IAM/CEDAR benchmark databases. Finally, future work and persist problems are highlighted. [ABSTRACT FROM AUTHOR]

Neural network are most popular in the research community due to its generalization abilities. Additionally, it has been successfully implemented in biometrics, features selection, object tracking, document image preprocessing and classification. This paper specifically, clusters, summarize, interpret and evaluate neural networks in document Image preprocessing. The importance of the learning algorithms in neural networks training and testing for preprocessing is also highlighted. Finally, a critical analysis on the reviewed approaches and the future research guidelines in the field are suggested. [ABSTRACT FROM AUTHOR]