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Reading Insurance Card

Reading information from Insurance Cards is a problem of OCR (Optical Character Recognition) for Japanese Words. In order to read the 3 information from Insurance Card, Deep Learning approach is used. In details, we use Tesseract engine to read all information from the card and then employ some combination/ modification features to improve the recognition results. The improvement is basically based on β€œtry and improve” process based on real data that we collected from Internet. β€’ Categorial information (for example, MNIST: 0-9) should follow a categorical distribution: 𝑐1βˆΌπΆπ‘Žπ‘‘ (𝐾=10, 𝑝=0.1) β€’ Shape information (rotation, width) should follow a uniform distribution, for example: 𝑐2, 𝑐3βˆΌπ‘ˆπ‘›π‘–π‘“ (βˆ’1,1)
Manufacturer: Japanese Company

Reading information from Insurance Cards is a problem of OCR (Optical Character Recognition) for Japanese Words. In order to read the 3 information from Insurance Card, Deep Learning approach is used. In details, we use Tesseract engine to read all information from the card and then employ some combination/ modification features to improve the recognition results. The improvement is basically based on β€œtry and improve” process based on real data that we collected from Internet. β€’ Categorial information (for example, MNIST: 0-9) should follow a categorical distribution: 𝑐1βˆΌπΆπ‘Žπ‘‘ (𝐾=10, 𝑝=0.1) β€’ Shape information (rotation, width) should follow a uniform distribution, for example: 𝑐2, 𝑐3βˆΌπ‘ˆπ‘›π‘–π‘“ (βˆ’1,1)

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