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Handwriting recognition and automatic scoring for descriptive answers in Japanese language tests
As QWK is over 0. 8, it represents an acceptable similarity of scoring between the automatic scoring system and the human examiners.
A Transformer-based Math Language Model for Handwritten Math Expression Recognition
Handwritten mathematical expressions (HMEs) contain ambiguities in their interpretations, even for humans sometimes.
GSSF: A Generative Sequence Similarity Function based on a Seq2Seq model for clustering online handwritten mathematical answers
Toward a computer-assisted marking for descriptive math questions, this paper presents clustering of online handwritten mathematical expressions (OnHMEs) to help human markers to mark them efficiently and reliably.
Global Context for improving recognition of Online Handwritten Mathematical Expressions
The classification model is trained by multiple paths of symbols and spatial relations derived from the Symbol Relation Tree (SRT) representation of HMEs.
Learning symbol relation tree for online mathematical expression recognition
The recognition system has two parts: a temporal classifier and a tree connector.
Text-independent writer identification using convolutional neural network
Randomly sampled tuples of images from the training set are used to train the CNN and aggregate the extracted local features of images from the tuples to form global features.
Online trajectory recovery from offline handwritten Japanese kanji characters
Although the visual verification reveals some problems, the recognition experiments demonstrate the effect of trajectory recovery in improving the accuracy of offline handwritten character recognition when online recognition for the recovered trajectories are combined.
