Seeding is a significant but challenging task for optimizing crystallization process operation, product quality, and process efficiency. With the development of modern crystallization technology, there have been considerable advancements in seeding techniques in recent years. This article elaborates on how seeds affect the crystallization process, clarifies the qualitative connection between seeding parameters, defines the variables and product quality attributes based on a variable-based causal graph, and analyzes various possible factors that result in seeding-technique failure based on an Ishikawa diagram. The development of seeding-technique studies and existing problems are then systematically reviewed and discussed based on the qualitative and quantitative characteristics of seeds and the factors that influence seeding efficiency. Based on extensive research accomplishments, the challenges and opportunities for utilizing seeding techniques are analyzed, and potentially valuable seeding-technique topics and directions for improving crystallization are presented. In addition, a framework to guide seed recipe design and optimization is defined by combining simulation and experimental verification.

License plate localization and character segmentation and recognition are the research hotspots of vehicle license plate recognition (VLPR) technology. A new method to VLPR is presented in this paper. In license plate localization section, Otsu binarization is operated to get the plate-candidates regions, and a text-line is constructed from the candidate regions. According to the text-line construction result and the characteristics of the license plate character arrangement, the license plate location will be determined. And then the locally optimal adaptive binarization is utilized to make more accurate license plate localization. After the license plate localization, the segment method of vertical projection information with prior knowledge is used to slit characters and the statistical features are extracted. Then the multilevel classification RBF neural network is used to recognize characters using the feature vector as input. The results show that this method can recognize characters precisely and improve the ability of license plate character recognition effectively

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