Results 221 to 230 of about 148,501 (286)
From Fragment to One Piece: A Review on AI-Driven Graphic Design. [PDF]
J ImagingZou X, Zhang W, Zhao N.
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Automatic detection and extraction of key resources from tables in biomedical papers. [PDF]
BioData MinOzyurt IB, Bandrowski A.
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Patch-sampled contrastive learning for dense prediction pretraining in metallographic images. [PDF]
Sci RepLi M, Liu Y, Chen D, Bao J, Huo Y.
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Subword symmetry in natural languages. [PDF]
R Soc Open SciPelloni O +4 more
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SideCow-VSS: A Video Semantic Segmentation Dataset and Benchmark for Intelligent Monitoring of Dairy Cows Health in Smart Ranch Environments. [PDF]
Vet SciYao L +6 more
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New Segmentation Algorithm for Individual Offline Handwritten Character Segmentation
, 2016 openaire +1 more source
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Word and Character Segmentation
2021In the previous chapter, the methods for text detection from natural scene and video image are discussed. To recognize text, the methods require characters. Therefore, this chapter focuses on word and character segmentation based on the space between words and characters.
Palaiahnakote Shivakumara, Umapada Pal
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SPIE Proceedings, 1984
In the Optical Character Reader (OCR) system design, the character segmentation technique is important. For example, the Automatic Mail Address Reader is required to manage printed characters of many font types and poor print quality. In this case, OCR performance will be affected by character segmentation technique.
Yoshitake Tsuji, Ko Asai
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In the Optical Character Reader (OCR) system design, the character segmentation technique is important. For example, the Automatic Mail Address Reader is required to manage printed characters of many font types and poor print quality. In this case, OCR performance will be affected by character segmentation technique.
Yoshitake Tsuji, Ko Asai
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Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems, 2003
In optical character recognition (OCR) and document analysis many errors are not caused by inadequate classifier power, but by segmentation errors. Besides broken characters, merged characters constitute the major remaining problem. This paper presents an efficient method for segmenting merged characters.
T. Bayer, U. Kressel, M. Hammelsbeck
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In optical character recognition (OCR) and document analysis many errors are not caused by inadequate classifier power, but by segmentation errors. Besides broken characters, merged characters constitute the major remaining problem. This paper presents an efficient method for segmenting merged characters.
T. Bayer, U. Kressel, M. Hammelsbeck
openaire +1 more source