The written character recognition (HCR) methodology still includes a big variety of applications. Across any union government like an Asian nation, reading communicating addresses in many states contains distinct languages. One vital application of HCR within the automatic industry altogether developed nations is the verification of order of payment amounts and signatures. The documents' optical character recognition is compared to written documents by an individual. Characters from many sorts of files, as well as image and word document files, square measure translated victimization this OCR. This study article's primary goal is to supply an answer for numerous handwriting recognition ways, as well as bit input via a mobile screen and picture files. Artificial neural networks applied math techniques, and different techniques square measure utilized by popular methodologies to handle issues that don't seem to be linearly dissociative. This analysis study uses a spread of comparison and recognition techniques to spot handwriting characters in image documents. in addition, the study contrasts the analytical way to support vector machine (SVM) heuristic network methodology with applied math, templet similarity, well-organized pattern identification, and comprehensive ways. The technique, depiction, and style of the Written Character Recognition System, as a system assessing and developing outcomes, also are coated within the article. The objective is to point out how well neural networks recognize handwriting characters.