The model first uses convolutional neural networks to extract spatial features, then shares them throughout the whole model. Two FCNs are used to extract character information to form a score map. Finally, character features are reused to adjust the accurate segmentation points in the score map.
Oct 9, 2020
Sep 28, 2020 · The model first uses convolutional neural networks to extract spatial features, then shares them throughout the whole model. Two FCNs are used ...
A Novel Semantic Segmentation Model for Chinese Characters. Gao, Zhenyu; ;; Liu, Jin; ;; Li, Yiyao; ;; Yang, Yihe; ;; He, Huihua. Abstract. Publication: IEEE ...
A Novel Semantic Segmentation Model for Chinese Characters. Character segmentation plays an important role in optical character recognition (OCR).
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In this paper, we propose an efficient semantic segmentation model based on label coding (LC), called LCSegNet, to recognize large-scale Chinese characters.
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We propose precise detection of Chinese characters using a deep reinforcement learning framework to obtain tighter bounding boxes under large IoU.
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We present a character-based model for joint segmentation and POS tagging for. Chinese. The bidirectional RNN-CRF ar- chitecture for general sequence ...