Abstract
Character recognition algorithm can directly affect the accuracy and speed of character recognition. This algorithm uses BP neural network to train samples, preserve neural network weights, and recognize photographed images. The software algorithm integrates image-processing and neural network modules. Image-processing modules include pre-treatment processes, such as, binaryzation, denoising, dilation, erosion, rotation and character segmentation and extraction of images collected by cameras. Neural network modules include network training, identification, display, saving, loading, and other modules, such as image preprocessing and recognition. A prototype of online engineering character recognition system has been developed. Test results indicate that the duration of a single picture is approximately 100 ms, and the detection time displayed by the interface includes the zooming time of display interface that is approximately 200 ms.
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Rong, C., Luqian, Y. (2018). Engineering Character Recognition Algorithm and Application Based on BP Neural Network. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_36
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DOI: https://doi.org/10.1007/978-3-319-93818-9_36
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