Automating the task of scoring short handwritten student essays is considered. The goal is to assign scores which are comparable to those of human scorers by coupling two AI technologies: optical handwriting recognition and automated essay scoring. The test-bed is that of essays written by children in reading comprehension tests. The process involves several image-level operations: removal of pre-printed matter, segmentation of handwritten text lines and extraction of words. Recognition constraints are provided by the reading passage, the question and the answer rubric. Scoring is based on using a vector space model and machine learning of parameters from a set of human-scored samples. System performance is comparable to that of scoring based on perfect manual transcription.
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