Handwritten characters and numerals are still challenging to read, despite decades of study on offline Indic recapitulations. The characters' uncanny facial likeness and the Indic scripts' pervasive structural similarity are to blame for this. Results for the identification of handwritten Indian writing using machine learning-based techniques are comparable to those for other computer vision tasks. This is the scenario, despite the fact that the problem is still fairly recent. However, developing a handcrafted Machine learning model that is efficient for various Indian languages requires considerable trial and error and in-depth knowledge with the issue. A solution was found after the search was streamlined using an evolving meta-heuristics approach. managed to improve our text extraction and language recognition abilities naturally by fusing machine learning and EasyOcr in this manner. Focused on Hindi, Malayalam, Kannada, as well as Tamil languages with Ensemble Learning models to detect languages present in images using the EasyOcr library, proposeddifferent models, including Ensemble learning voting Classifier,Multi-layer perceptron and Support vector machine at accuracy 98.6% as well as 89.9% with 100% detection and text extraction rate of Hindi, Kannada, Malayalam and Tamil Languages.

Machine Learning, Voting Classifier, Ensemble learning, Ada Boost, Multi-Layer Perceptron, Script Identification, Easy OCR.