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• W2987271990 abstract "This paper introduces a lightweight model for printed and handwritten text identification in document images, which is then deployed as a web service to enable easy integration into existing workflows. Identifying printed and handwritten text in documents containing multiple handwritten annotations, which partially overlap machine printed text, is a challenging task. The lack of existence of a dataset containing pixel-level annotations for this task explains why the many papers tackling this problem employ word-or line-level classification, which cannot effectively solve the task mentioned above. In this work, we use a newly created dataset containing pixel-level annotations to train a lightweight, fully convolutional network, which we combine with a conditional random field for postprocessing. We measure the performance of our method on the aforementioned dataset and compare it to results achieved by the U-net fully convolutional architecture. Initial test results indicate a 90% mean IoU, which is a 5% improvement when comparing to the results produced by the U-net model after postprocessing. Hence, our contribution is two-fold. First, we introduce a model which combines a lightweight fully convolutional architecture with conditional random field postprocessing to solve the task of printed and handwritten text recognition on a pixel-level, and secondly, we describe how our model is deployed as a web service." @default.
• W2987271990 created "2019-11-22" @default.
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• W2987271990 date "2019-09-01" @default.
• W2987271990 modified "2023-09-26" @default.
• W2987271990 title "PHTI-WS: A Printed and Handwritten Text Identification Web Service Based on FCN and CRF Post-Processing" @default.
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• W2987271990 doi "https://doi.org/10.1109/icdarw.2019.10033" @default.
• W2987271990 hasPublicationYear "2019" @default.
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