Handwritten content recognition is the skill to transliterate the text input encased in reports or pictures into digitally advanced content. The content example can change from dialect to dialect. Human composed content includes a wide arrangement of varieties, for instance, couple of languages have characters segregated from one another while a couple of languages incorporate cursive organizations. Along these lines, making it profoundly difficult to precisely recognize transcribed contents. Customarily, recognizing transcribed contents was done through character segmentation, feature extraction, or character acknowledgment. With changing occasions and developing innovations, neural networks - a machine learning approach has helped in characterizing and grouping transcribed messages massively. This paper tries to decipher a person`s manually written content to computerized organize utilizing a neural system approach. Simulating a neural network to recognize written by hand content would help in accomplishing unrivalled exactness, and make an enhanced and quick calculation. The cutting-edge approaches focus on extracting features by eliminating distortions in addition to the commotion, and later anticipate the conceivable outcomes of that specific character. The way toward recognizing written by hand message has been distinguished as one of the high-flying tests in the field of characteristic natural language processing, machine learning, and computer vision applications.

Handwritten Text Recognition, machine learning, neural network, image recognition

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