5 results about "Letter recognition" patented technology

This invention discloses a license plate recognition method and system based on deep learning, comprising the following steps: acquiring license plate image data to generate an image dataset; performing semantic decomposition to divide the data into region code units, letter units, and number units, and constructing a semantic structure graph; constructing a multi-path character recognition model with an improved PCNN model as the backbone feature structure; inputting the image dataset into the multi-path character recognition model respectively, and outputting region code recognition results, letter recognition results, and number recognition results to generate candidate license plate recognition results; if there are illegal character combinations or missing structural unit information, performing path filtering processing to generate a candidate path set; performing inference imputation processing to generate a complete path set; and merging and outputting the license plate recognition results and the set of confident candidate paths. This invention improves the accuracy of character recognition while enhancing robustness to structural defects, possessing the advantages of high recognition accuracy and strong fault tolerance.

The invention discloses an English letter recognition method and device based on machine vision and a medium, and relates to the technical field of image recognition, and the method comprises the steps: collecting an original image, carrying out the graying and denoising processing, obtaining a denoised gray-scale image, carrying out the correction and binarization processing of the denoised gray-scale image, and delimiting a connected domain, and obtaining a binary letter image; iteratively trimming the binary alphabet graph, setting nodes and edges, summarizing the nodes and the edges into a skeleton graph structure, calculating the path length of the edges and the curvature and the splitting degree of the nodes, and labeling the path length and the curvature and the splitting degree on the skeleton graph structure to obtain a skeleton graph structure with topological filtering labeling; constructing input of a graph neural network model according to a set topological complex of the skeleton graph structure with the topological filtering label, and completing training of the graph neural network model; and inputting the node feature matrix into the graph neural network model to obtain a letter recognition result. According to the invention, through completing the training of the graph neural network model, the recognition accuracy of complex, scribbling or deformed letters is improved.

The application provides a handwriting recognition method and system based on ultrasonic waves, a mobile terminal and a server, the application combines ultrasonic waves with handwriting recognition, proposes a new input method, and through machine learning technology combining CTC loss and LSTM, realizes real-time recognition of handwriting input; the ultrasonic waves after being carried by the autocorrelation sequence improve the robustness to environmental changes, and in a cross-environment experiment, a high letter recognition accuracy is achieved; the machine learning model has good segmentation ability for the interval between words, can support continuous handwriting input recognition, solves the problem that long sequence input is difficult to segment in actual application, and partially solves the cross-environment problem faced by passive sensing, and shows great potential of ultrasonic wave passive recognition in commercial fields.

The application discloses an English letter recognition method and device based on machine vision and a medium, relates to the technical field of image recognition, and comprises the following steps: collecting an original image, performing grayscale and denoising processing to obtain a denoised grayscale image, correcting the denoised grayscale image and performing binaryzation processing, delimiting a connected domain, and obtaining a binary letter image; performing iterative pruning on the binary letter image, setting nodes and edges, summarizing a skeleton graph structure, calculating the path length of the edges and the curvature and bifurcation degree of the nodes, and labeling the skeleton graph structure to obtain a skeleton graph structure with topological filtering labels; setting a topological complex according to the skeleton graph structure with topological filtering labels, constructing the input of a graph neural network model, and completing the training of the graph neural network model; and inputting a node feature matrix into the graph neural network model to obtain a letter recognition result. Through the training of the graph neural network model, the recognition accuracy of complex, careless or deformed letters is improved.

This invention belongs to the field of character recognition technology and provides a handwritten letter recognition method based on piezoresistive signal detection, including the following steps: collecting the temporal piezoresistive signal during the letter writing process as raw data; preprocessing the raw data to obtain training data; constructing a recognition model, inputting the training data into the recognition model to extract temporal feature vectors, and performing classification training; using the trained recognition model for forward inference to confirm the letter category; this invention accurately captures changes in writing force and temporal logic based on piezoresistive electrical signals, significantly improving the accuracy of handwritten letter recognition.