Automated Grading of Handwritten Essays

Abstract

Automatic grading of handwritten essays is vital in evaluating the performance of students in educational settings, particularly in situations where language experts are rare. We build a system capable of taking the input as handwritten essays in image format and outputs the grading on the scale of 0-5; 0 being the worst and 5 being the best. The overall system integrates Optical Handwriting Recognition (OHR) and Automated Essay Scoring (AES)/grading. The handwritten essay is transcribed using a network composed of Multi-Dimensional Long Short Term Memory (MDLSTM) and convolution layers. The loss function is Connectionist Temporal Classification (CTC). The AES model is a 2-layer artificial neural network with a feature set based on pretrained GloVe word vectors. The results of grading of essays are compared for transcriptions of essays received from OHR system and transcriptions of essays done manually. The mutual agreement between the two shows a Quadratic Weighted Kappa score of 0.88. The results indicate that though the current OHR systems have transcription errors but as a whole can perform well for an application like AES.