FTPN: Scene Text Detection With Feature Pyramid Based Text Proposal Network
نویسندگان
چکیده
منابع مشابه
Feature Enhancement Network: A Refined Scene Text Detector
In this paper, we propose a refined scene text detector with a novel Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement. Retrospectively, both region proposal with only 3 × 3 sliding-window feature and text detection refinement with single scale high level feature are insufficient, especially for smaller scene text. Therefore, we design a new FEN network with ta...
متن کاملDetecting Text in Natural Image with Connectionist Text Proposal Network
We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image. The CTPN detects a text line in a sequence of fine-scale text proposals directly in convolutional feature maps. We develop a vertical anchor mechanism that jointly predicts location and text/non-text score of each fixed-width proposal, considerably improving localization accuracy...
متن کاملFused Text Segmentation Networks for Multi-oriented Scene Text Detection
In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instanceaware segmentation perspective. We present Fused Text Segmentation Networks, which combine multi-level features during feature extracting as text instance may rely on finer feature expression compared to general objects. It detects and segments the text instance jointly and simultaneous...
متن کاملText-Attentional Convolutional Neural Networks for Scene Text Detection
Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this wor...
متن کاملPerspective Scene Text Recognition with Feature Compression and Ranking
In this paper we propose a novel character representation for scene text recognition. In order to recognize each individual character, we first adopt a bag-of-words approach, in which the rotation-invariant circular Fourier-HOG features are densely extracted from an individual character and compressed into middle level features. Then we train a set of two-class linear Support Vector Machines in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2908933