BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection

Arbitrary shape scene text detection is of great importance in scene understanding tasks. Due to the complexity and diversity of text in natural scenes, existing scene text algorithms have limited accuracy for detecting arbitrary shape text. In this paper, we propose a novel arbitrary shape scene text detector through boundary points dynamic optimization(BPDO). The proposed model is designed with a text aware module (TAM) and a boundary point dynamic optimization module (DOM). Specifically, the model designs a text aware module based on segmentation to obtain boundary points describing the central region of the text by extracting a priori information about the text region. Then, based on the idea of deformable attention, it proposes a dynamic optimization model for boundary points, which gradually optimizes the exact position of the boundary points based on the information of the adjacent region of each boundary point. Experiments on CTW-1500, Total-Text, and MSRA-TD500 datasets show that the model proposed in this paper achieves a performance that is better than or comparable to the state-of-the-art algorithm, proving the effectiveness of the model.Comment: Accepted to ICASSP 202

Text Region Multiple Information Perception Network for Scene Text Detection

Segmentation-based scene text detection algorithms can handle arbitrary shape scene texts and have strong robustness and adaptability, so it has attracted wide attention. Existing segmentation-based scene text detection algorithms usually only segment the pixels in the center region of the text, while ignoring other information of the text region, such as edge information, distance information, etc., thus limiting the detection accuracy of the algorithm for scene text. This paper proposes a plug-and-play module called the Region Multiple Information Perception Module (RMIPM) to enhance the detection performance of segmentation-based algorithms. Specifically, we design an improved module that can perceive various types of information about scene text regions, such as text foreground classification maps, distance maps, direction maps, etc. Experiments on MSRA-TD500 and TotalText datasets show that our method achieves comparable performance with current state-of-the-art algorithms.Comment: Accepted to ICASSP 202

CMFN: Cross-Modal Fusion Network for Irregular Scene Text Recognition

Scene text recognition, as a cross-modal task involving vision and text, is an important research topic in computer vision. Most existing methods use language models to extract semantic information for optimizing visual recognition. However, the guidance of visual cues is ignored in the process of semantic mining, which limits the performance of the algorithm in recognizing irregular scene text. To tackle this issue, we propose a novel cross-modal fusion network (CMFN) for irregular scene text recognition, which incorporates visual cues into the semantic mining process. Specifically, CMFN consists of a position self-enhanced encoder, a visual recognition branch and an iterative semantic recognition branch. The position self-enhanced encoder provides character sequence position encoding for both the visual recognition branch and the iterative semantic recognition branch. The visual recognition branch carries out visual recognition based on the visual features extracted by CNN and the position encoding information provided by the position self-enhanced encoder. The iterative semantic recognition branch, which consists of a language recognition module and a cross-modal fusion gate, simulates the way that human recognizes scene text and integrates cross-modal visual cues for text recognition. The experiments demonstrate that the proposed CMFN algorithm achieves comparable performance to state-of-the-art algorithms, indicating its effectiveness.Comment: Accepted to ICONIP 202

Centrosome amplification disrupts renal development and causes cystogenesis

International audienc

catena-Poly[[[aqua(7-hydroxy-2H-1-benzopyran-2-one)sodium]-di-μ-aqua] 2-oxo-2H-1-benzopyran-7-olate monohydrate]

The asymmetric unit of the title compound, {[Na(C9H6O3)(H2O)3](C9H5O3)·H2O}n, contains two crystallographically independent Na atoms, two 7-hy­droxy­coumarin ligands, six coordinated water mol­ecules, two 7-hy­droxy­coumarin anions and two uncoordinated water mol­ecules. Both Na atoms exhibit a distorted octa­hedral coordination geometry and are coordinated by five water O atoms and the terminal O atom from a 7-hy­droxy­coumarin ligand. Four of the water mol­ecules are bridging, whereas the fifth is terminal. Na—O bond distances are in the range 2.288 (2)–2.539 (2) Å. In the chains, extending parallel to [100], adjacent Na atoms are separated by 3.60613 (7) Å. The uncoordinated water mol­ecules and 7-hy­droxy­coumarin phenolate anions are located between the chains and are hydrogen bonded to the chains

Stable Li Metal Anode Enabled by Space Confinement and Uniform Curvature through Lithiophilic Nanotube Arrays

The application of lithium (Li) metal anodes in rechargeable batteries is primarily restricted by Li dendrite growth on the metal’s surface, which leads to shortened cycle life and safety concerns. Herein, well‐spaced nanotubes with ultrauniform surface curvature are introduced as a Li metal anode structure. The ultrauniform nanotubular surface generates uniform local electric fields that evenly attract Li‐ions to the surface, thereby inducing even current density distribution. Moreover, the well‐defined nanotube spacing offers Li diffusion pathways to the electroactive areas as well as the confined spaces to host deposited Li. These structural attributes create a unique electrodeposition manner; i.e., Li metal homogenously deposits on the nanotubular wall, causing each Li nanotube to grow in circumference without obvious sign of dendritic formation. Thus, the full‐cell battery with the spaced Li nanotubes exhibits a high specific capacity of 132 mA h g−1 at 1 C and an excellent coulombic efficiency of ≈99.85% over 400 cycles.This work presents a technique for suppressing lithium dendrite formation through ultrauniform curvature and space confinement. Lithium uniformly deposits/dissolves on the nanotube surfaces where the local current distribution is uniform due to the ultrauniform curvature. The nanotube spacing provides confined spaces to host deposited lithium. Thus, a full‐cell battery with spaced lithium nanotubes shows excellent specific capacity at high rates.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153606/1/aenm201902819_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153606/2/aenm201902819-sup-0001-SuppMat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153606/3/aenm201902819.pd

End-to-end scene text detection and recognition algorithm based on Transformer decoders

Aiming at the detection and recognition task of arbitrary shape text in scene, a novelty scene text detection and recognition algorithm which could be trained by end-to-end algorithm was proposed.Firstly, the detection branch of text aware module based on segmentation idea was introduced to detect scene text from visual features extracted by convolutional network.Then, a recognition branch based on Transformer vision module and Transformer language module encoded the text features of the detection results.Finally, the text features encoded by the fusion gate in the recognition branch were fused to output the scene text.The experimental results on the three benchmark datasets of Total-Text, ICDAR2013 and ICDAR2015 show that the proposed algorithm has excellent performance in recall, precision, F-score, and has certain advantages in efficiency