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This paper presents an experiment of automatically scoring handwrittendescriptive answers in the trial tests for the new Japanese university entranceexamination, which were made for about 120,000 examinees in 2017 and 2018.There are about 400,000 answers with more than 20 million characters. Althoughall answers have been scored by human examiners, handwritten characters are notlabeled. We present our attempt to adapt deep neural network-based handwritingrecognizers trained on a labeled handwriting dataset into this unlabeled answerset. Our proposed method combines different training strategies, ensemblesmultiple recognizers, and uses a language model built from a large generalcorpus to avoid overfitting into specific data. In our experiment, the proposedmethod records character accuracy of over 97% using about 2,000 verifiedlabeled answers that account for less than 0.5% of the dataset. Then, therecognized answers are fed into a pre-trained automatic scoring system based onthe BERT model without correcting misrecognized characters and providing rubricannotations. The automatic scoring system achieves from 0.84 to 0.98 ofQuadratic Weighted Kappa (QWK). As QWK is over 0.8, it represents an acceptablesimilarity of scoring between the automatic scoring system and the humanexaminers. These results are promising for further research on end-to-endautomatic scoring of descriptive answers.

Handwriting recognition and automatic scoring for descriptive answers in Japanese language tests