They are a vital component of educational institutions' attempts to help students develop their personalities and improve classroom learning. Co-curricular activities are extracurricular activities that support and enhance the academic or core curriculum. The purpose of this study is to determine the relationship between extracurricular activities and student performance. A performance of approximately 90% correct recognition rate is achieved.The study project named 'Effects of Co-Curricular Activities on Student's Academic Performance Through Machine Learning' examines the effect of co-curricular activities on a student's academic performance. The classifier is trained and tested for printed Hindi texts. For developing the neural classifier, back-propagation neural network with two hidden layer is used. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. In this work, three feature extraction techniques: histogram of projection based on mean distance, histogram of projection based on pixel value and vertical zero crossing has been used to improve the rate of recognition. The basic symbols, obtained as the fundamental unit from segmentation process are recognized by neural classifier.

The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of document´s textual contents into paragraphs, lines, words and then at the level of basic symbols. Preprocessing, character segmentation, feature extraction and finally classification & recognitions are the major steps which are followed by a general OCR.

Presence of touching characters in the scanned documents further increase the segmentation process thus creating a major problem while designing an effective character segmentation technique. One of the major reasons for the poor recognition rate is error in character segmentation. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN) which improves the efficiency. As there is no separation between the characters of the text written in Hindi similar to texts written in English, the Optical Character Recognition (OCR) systems developed for Hindi language carries a very poor recognition rate. 'Hindi is the most spoken languages in India, with more than 300 million speaking it.

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