earticle

영어

As deep learning technology has improved in recent years, it has expanded from text-oriented document analysis to unstructured data such as images and tables, and there are an increasing number of studies on extracting meaning from such data and analyzing documents. Among them, there are various studies that analyze chart images because charts provide a lot of information such as checking or comparing abnormal elements of data by graphically representing various types of data. Chart classification is an important step because each category has a different way of extracting data and extracting and interpreting the meaning accordingly, so the focus is on improving the classification performance of deep learning-based classification models for various chart categories. However, deep learning-based models have the problem that experts need to allocate a lot of time to configure the model design optimized for the data and check the performance of the model after training. As a way to alleviate these problems, in this paper, we studied a chart classification model using a Neural Architecture Search technique that automatically explores the model structure optimized for the data. The optimal network structure was explored, trained, and tested using 69,600 chart data consisting of 12 chart categories, and the performance was compared with chart classification models using VGG-16 and ResNet-50 algorithms as a way to check the performance of the model. The average classification performance of the model using Neural Architecture Search showed higher classification accuracy than other models with Precision 99.7%, Recall 99.6%, and F1-Score 99.9%.