Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Fei Yin"'
Publikováno v:
ICDAR
Maximally stable extremal region (MSER) is popularly used for candidate character candidate extraction in scene text detection. Its requirement of maximum stability hinders high performance on images of high variability. In this paper, we propose a n
Publikováno v:
ICDAR
Scene text detection in videos has many application needs but has drawn less attention than that in images. Existing methods for video text detection perform unsatisfactorily because of the insufficient utilization of spatial and temporal information
Publikováno v:
ICDAR
The Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) has been demonstrated successful in handwritten text recognition of Western and Arabic scripts. It is totally segmentation free and can be trained directly from text line images. However,
Publikováno v:
ICDAR
The digitization of Chinese historical documents poses a new challenge that in the huge set of character categories, majority of characters are not in common use now and have few samples for training the character classifiers. To settle this problem,
Autor:
Nibal Nayef, Yuan Feng, Cheng-Lin Liu, Fei Yin, Umapada Pal, Joseph Chazalon, Zhenbo Luo, Dimosthenis Karatzas, Muhammad Muzzamil Luqman, Christophe Rigaud, Imen Bizid, Jean-Marc Ogier, Jean-Christophe Burie, Wafa Khlif, Hyun-Soo Choi
Publikováno v:
ICDAR
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Nov 2017, Kyoto, Japan. pp.1454-1459, ⟨10.1109/ICDAR.2017.237⟩
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Nov 2017, Kyoto, Japan. pp.1454-1459, ⟨10.1109/ICDAR.2017.237⟩
Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to det
Publikováno v:
ICDAR
In handwriting recognition, the test data usually come from multiple writers which are not shown in the training data. Therefore, adapting the base classifier towards the new style of each writer can significantly improve the generalization performan
Publikováno v:
ICDAR
Page segmentation is a fundamental and challenging task in document image analysis due to the layout diversity. In this work, we propose a pixel-wise segmentation method for historical handwritten documents using fully convolutional network (FCN). Th