Poly(lactic acid) (PLA) as one of the most promising biodegradable polymers is being tremendously restricted in large-scale applications by the notorious toughness, ductility, and heat distortion resistance. Manufacturing PLA with excellent toughness, considerable ductility, balanced strength, and great heat distortion resistance simultaneously is still a great challenge. Natural structural materials usually possess excellent strength and toughness by elaborately constructed sophisticated hierarchical architectures, however, completely reproducing natural structural materials' architecture have evidenced to be difficult. Inspired by the hierarchical construction of the compact bone, an innovational method with an intensive and continuous elongational flow field and facile annealing process was developed to create bonemimicking structured PLA at an industrial scale. The bone-mimicking structured PLA with unique and novel hierarchical architectures of interlocked 3D network lamellae and large extended-chain lamellae connecting the regular lamellae was constructed by in situ formed oriented thermoplastic poly(ether)urethane nanofibers (TNFs) acting as "collagen fibers", orderly staggered PLA lamellae behaving as "hydroxyapatite (HA) nanocrystals", and the tenacious interface functioning as a "soft protein" adhesive layer. Attributed to the unique structure, it possesses super toughness (90.3 KJ/m 2 ), high stiffness (2.15 GPa), balanced strength (52.6 MPa), and notable heat distortion resistance (holding at 163 °C for 1 h) simultaneously. These excellent performances of the structured PLA provide it with immense potential applications in both structural and bio-engineering materials fields such as artificial bones and tissue scaffolds.
The increasing use of invoicing has created an unnecessary burden on labor and material resources in the financial sector. This paper proposes a method to intelligently identify invoice information based on template matching, which retrieves the required information by image preprocessing, template matching, an optical character recognizing, and information exporting. The origin invoice image is preprocessed first to remove the useless background information by secondary rotation and edge cutting. Then, the region of the required information in the obtained regular image is extracted by template matching, which is the core of the intelligent invoice information identification. The optical character recognizing is utilized to convert the image information into text so that the extracted information can be directly used. The text information is exported for backup and subsequent use in the last step. The experimental results indicate that the method using normalized correlation coefficient matching is the best choice, demonstrating a high accuracy of 95%, and the average running time of 14 milliseconds. INDEX TERMS Invoice information identification, template matching, contour extraction, image processing, convolutional neural network.
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