History of human civilization is very old. Right from its start, man tried to record events which were exciting to him in some sort of script. Through the script information of contemporary events and their explanation can be easily transferred to the next generation. For thousands of years, human society has scripted its knowledge and their interpretation of things around them. Since no digital medium was available at that time, they recorded their knowledge on different mediums such as large leaves of trees. Sometime later, modern paper was invented. The biggest disadvantage of writing things on paper is that paper gets destroyed over time and things written on it are lost. Even today, we are writing most of the things on paper. However, a lot of digital platforms are available to keep all these information safe. For decades, our researchers are trying to convert such high volume of handwritten scripts to digital form. However, different scripting styles and practices do not allow to simply convert all the handwritten information to its digital form. Segmentation is a critical issue to be solved among all many aspects. Specially, when the overlapping and crossed alphabets, it is very hard to segment the words and characters from each other. Even today, there is no simple segmentation techniques available which can segment the alphabets, numbers and symbols in found in handwriting and to decipher its real meaning automatically. So, more sophisticated algorithms are needed to handle the current challenges occurring in the handwritten scripts’ digitization. As different machine learning or deep learning algorithms are invented so the researchers are adopting different segmentation free strategies to recognize the handwritten scripts to convert them in digital form. This paper delineates the state-of-the-art segmentation free handwritten scripts recognition methodologies. Various segmentation free script recognition methodologies are explained with their merits and de-merits. This paper focuses on the methods which can bypass the preprocessing requirements. The use of such segmentation free recognition methods is elucidated with examples.