Hindi is a national language of India spoken in many states in our countries, like Bihar, Uttar Pradesh, Madhya Pradesh, Jharkhand, and Delhi. The Hindi language is 3rd most popular language globally, which is the script of Devanagari. It consists of 36 primary alphabets and ten digits. We present sophisticated handwritten Hindi character recognition (2HCR) using machine learning techniques to implement Hindi characters and digits. A dataset consists of Ninety-Two Thousand images of 46 different types of characters and digits in the Hindi language segmented from handwritten documents. Nowadays, it has become easy totrain data because of the availability of various algorithms and methodology. We have used many classification algorithms for implementing and improving accuracy. Classification algorithms are Linear-Regression (LR), Logistic-Regression (LGR), Support-VectorMachine (SVM), Random-Forest (RF), and Naïve-Bayes (NB) to classify the model and improve the accuracy. Handwritten Character Recognition, the area for research is still an active platform because of individuals’ different human writing styles, shapes, and sizes. Also, it is used in many applications such asreading license plate numbers, document reading, cheque numbers, postcodes on envelopes, verification of signatures, etc. This system, that we have developed, designed, and implement, has been done using python programming. After completing, we analyzed the performance and accuracy of thesystem.

Keywords : Machine Learning, Python, 2HCR, OCR, Hindi Character, Devanagari, LR, LGR, SVM, RF, NB.