201 results about "Digit recognition" patented technology

The invention relates to an image recognition method, in particular to an automatic meter-reading image recognition method, and aims at solving the technical problem of low accuracy and proneness to digital recognition error. The automatic meter-reading image recognition method is simple and convenient to use, capable of accurately recognizing dial plate images and high in accuracy. The technical scheme includes the following steps: a, collecting images of a dial plate by an image collecting device; b, roughly positioning a digit indicating image; c, accurately positioning a digit indicating area image; d, accurately positioning a pointer area image; e, preprocessing the images; f, performing digit division; g, performing digit recognition; h, recognizing angle of a pointer. The automatic meter-reading image recognition method is widely used for recognition of digits of the dial plate in a remote meter reading system.

The invention provides a recognition method for English words and digits in a natural scene image. According to the method, recognition of the English words and the digits in a natural scene is divided into a feature extraction step, a feature focusing step and a feature recognition step, wherein a convolutional neural network is utilized to perform feature extraction on an input image, an attention mechanism is utilized to focus on useful information in a feature sequence, a long and short-time memory network is utilized to recognize feature vectors, therefore, a deep neural network and the attention mechanisms are combined, and a final recognition result can be obtained directly when the image is input into the deep neural network. Through the method, it is not needed to perform winding sliding operation on the input image or recognize characters in a window; and meanwhile, an output string is the final recognition result, so that it is not needed to integrate recognized strings through a merging algorithm.

The invention provides a digit recognition method for identification cards. The digit recognition method comprises the following steps: acquiring a classifier training file; transforming a color image to be identified into a gray level image; accurately positioning digit element positions of each identification card; accurately segmenting digit characters of each identification card; verifying whether a candidate region is a real digit region of each identification card; carrying out digit recognition on the basis of a nearest neighbor algorithm; judging whether the current digit element is the last group of digit element. According to the digit recognition method provided by the invention, positioning, segmentation and identification of digits on the identification cards are carried out by adopting an image processing technology; the recognition precision is relatively high, no special equipment is needed and the cost is low; the digit recognition method has strong applicability to various environments and can be used for reading information of multiple identification cards simultaneously.

The invention relates to amethod and device for identifying the digital image displayed in the mechanical instrument window, suitale for the identification of figure for various mechanical instrument window. The method, based on the working principle of absolute encoder in the field of industrial sensor, applys the mechanical location identifying method to the field of identifying image, carry out the identification for the position of image to reach the identification for the content of image, and especially is suitale for the application in which the one-chip microcomputer is as a processor, with high efficiency, accurate identification and good versatilityin. The device adopts the one-chip microcomputer as processor and is an integer of collecting, processing, identifying and transmitting image, and characterized in that the device comprises an image collection module, one-chip microcomputer processing and identifying module, a communication interface, a power module and transparent sealing structure. The device, having low cost, high efficiency and little volume, can be installed above the various mechanical instrument window with other structure, accordingly the identification of figure displayed in the instrument window can be carried out, as well as the date and the image are transmitted at the same time.

The present invention provides a handwritten digital recognition method and system based on depth learning. A convolutional neural network is trained with a sample set which is formed by constructing a handwritten digital picture with a label, the trained convolutional neural network is saved, and the image to be identified is taken as the input, and according to the output vector, the recognition result is obtained. The handwritten digital image is identified through the convolution neural network, so that the displayed feature extraction is avoided, the picture is directly taken as the network input, and the recognition accuracy is high. The network can be repeatedly used after being trained, the processing efficiency is high and the training time is short.

The invention discloses a handwritten numeral recognition method based on point density weighting online FCM clustering. The method is used for processing the large-scale offline handwritten numeral recognition problem. The method includes the steps that (1), all handwritten numeral image sets are preprocessed; (2), clustering centers are initialized, and data points are made to sequentially enter processing procedures; (3), the membership degree of the current data point and all the clustering centers is calculated; (4), if the membership degree reaches a threshold value, the position of the nearest clustering center is updated; (5), if the membership degree does not reach the threshold value, the current data point is not processed and is temporarily placed in a to-be-processed region; (6), when the to-be-processed region reaches certain standards, data in the to-be-processed region are clustered through a point density weighting FCM algorithm, and the clustering centers are updated; (7), circulation continues until all the data points are processed; (8), the membership degrees of all the data points are calculated through acquired clustering center blocks, the data points are divided into different classes, and data classification is finished through scanning at a time. According to the method, the space complexity and the time complexity can be lowered from the aspect of processing the large-scale handwritten numeral recognition problem.

A brain-computer interface (BCI) designed with a hybrid electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for letter and number recognition system for people who cannot speak. By this system, the words and numbers a subject thinks of are reflected on a display screen. A speech tool has been designed for these individuals allowing them to express themselves.

The invention discloses a multifunctional digital meter automatic reading method based on a target detection algorithm, and the method comprises the following steps: S1, carrying out the lightweight improvement of a YOLOv4 network model, and obtaining a YOLOv4 lightweight network model; S2, collecting the multifunctional digital table picture, and performing position calibration and category calibration on numbers on the multifunctional digital table picture; and S3, training the YOLOv4 lightweight network model by using the multifunctional digital table picture calibrated in the step S2 and the calibration information of the multifunctional digital table picture to obtain a YOLOv4 lightweight digital detection model. The multifunctional digital meter automatic reading method based on thetarget detection algorithm is high in recognition speed, high in recognition accuracy and good in stability, and the problems that an existing digital meter automatic reading method is prone to beingaffected by external environment changes and image imaging quality and lacks stability and accuracy of digital recognition are solved.

The invention discloses a handwritten digit recognition method and device on the basis of image covariance characteristics. The method includes: extracting covariance matrixes of N handwritten digit training images, and using the covariance matrixes as training samples, wherein N is a natural number; utilizing a support vector machine to train the training samples to obtain classifier model coefficients, wherein the kernel function adopted by the support vector machine is a matrix kernel function for calculating similarity among the covariance matrixes; utilizing the classifier model coefficients, the matrix kernel function and the training samples to build a classifier model; utilizing the classifier model to recognize handwritten digit images which need to be recognized. The support vector machine adopting the matrix kernel function can be used for processing the image covariance characteristics, therefore the generated classifier model coefficients are more accurate, and recognition rate of handwritten digits is improved further.

The invention relates to the technical field of image recognition, in particular to a handwritten numeral recognition method based on feature dimension reduction, which comprises the following steps:obtaining a handwritten numeral image for recognition, and obtaining a preprocessed handwritten numeral image after image preprocessing; extracting handwritten numeral features in the preprocessed handwritten numeral image through feature extraction processing; through feature dimension reduction processing, reducing the feature dimension of the handwritten numeral features, and obtaining low-dimension handwritten numeral features; and performing feature recognition processing on the low-dimensional handwritten numeral features to obtain a handwritten numeral recognition result. According to the handwritten numeral recognition method based on feature dimension reduction, feature dimension reduction processing can be carried out on handwritten numeral features so as to reduce the difficultyand complexity of feature recognition, and therefore the recognition effect and efficiency of handwritten numerals can be improved.

The invention discloses a depth learning based smart instrument detection method. The method includes taking a picture of an instrument through a camera, performing preprocessing on the image throughan IPC and reading instrument numerical value through image number recognition technology; performing analysis on the read instrument numerical value data and making result prediction and judgment; sending an instrument change alarm if instrument damage is judged through analysis and taking corresponding halt treatment; sending an alarm and stopping equipment operation immediately if the instrument works normally but the numerical value surpasses a safety range of the instrument; performing smart learning on history data and previous judgment and returning to the system, improving data analysis and system pre-judgment continuously, improving the pre-judgment accuracy through uninterrupted machine self learning. The method provided by the invention has advantages of high smart level and good pre-judgment capability. Besides, the pre-judgment method is improved continuously through history data and history judgment and the smart pre-judgment capability is improved.

The invention discloses a gas meter character wheel number identification method, relates to a number identification method, and is particularly suitable for carrying out centralized identification ondigital pictures with uniform styles on a gas meter character wheel. The method comprises the following steps: acquiring a digital image from a picture, and identifying the digital image through a trained neural network; When a digital image is acquired, firstly, determining a red area and a black area on a photo, dividing then the photo into single numbers, and acquiring three-dimensional digital images after processing; And training the neural network model by using the three-dimensional digital image, identifying the to-be-identified digital image, and fusing identification results of several neural networks to complete identification. The method comprises the following steps: firstly, identifying a red region on a photo, and quickly positioning a region where a digital image is located; During the period of obtaining the digital image, unrecognized photos can be removed; The to-be-identified digital area adopts secondary positioning, so that the obtained result is more accurate; Different samples are adopted as a training set in different dimensions, three network models with different recognition effects are obtained for recognition, and the recognition accuracy is improved after recognition results of the three networks are fused.