CN114187598B - Handwriting digital recognition method, handwriting digital recognition equipment and computer readable storage medium - Google Patents
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Abstract
The invention discloses a handwriting digital recognition method, a handwriting digital recognition system, handwriting digital recognition equipment and a computer readable storage medium, and belongs to the technical field of quantum computing. The handwritten number recognition method comprises the following steps: acquiring target characteristic information of the picture to be identified, inputting the target characteristic information into a trained target classical neural network, and outputting a target parameter value corresponding to the target handwriting number as a parameter value of a quantum logic gate; determining a target quantum neural network according to the parameter value of the quantum logic gate, and calculating a sub-quantum state psi through a quantum circuit corresponding to the target quantum neural network i Corresponding probability C i The method comprises the steps of carrying out a first treatment on the surface of the According to the sub-quantum state psi i Corresponding probability C i And determining a decimal value corresponding to the target handwritten number as a recognition result of the picture to be recognized. According to the invention, the digital reading of the picture containing the handwritten number can be realized by combining the quantum circuit of the target quantum neural network with the target classical neural network.
Description
Technical Field
The invention belongs to the technical field of quantum computing, in particular to a method for identifying handwritten numbers, and particularly relates to a method, equipment and a computer readable storage medium for identifying pictures containing the handwritten numbers.
Background
With the rapid development of computer technology and digital image processing technology in recent years, handwriting digital recognition technology has a far-reaching application demand in the fields of large-scale data statistics, mail sorting, finance, tax, finance and the like.
At present, the automatic input of data can be realized by inputting a picture with handwriting numbers into a trained digital recognition model, for example, a picture with 28×28 pixels is input, and what number on the picture is represented by a label value M' is recognized and output, so that the labor cost can be greatly reduced.
However, how to apply the quantum computing technology to the handwriting digital recognition field needs to be explored and solved.
Disclosure of Invention
The present invention provides a handwritten numeral recognition method, apparatus and computer-readable storage medium for solving the above problems, which can recognize a picture containing a handwritten numeral by using a quantum line corresponding to a target quantum neural network in combination with the neural network, the method comprising:
when a digital input instruction triggered by user operation is received, handwriting track data of a current user in a first identification area of a terminal interface is obtained, and a target handwriting number corresponding to the handwriting track data is displayed in the first identification area and used as a picture to be identified;
When a digital identification instruction is received, acquiring target characteristic information of the picture to be identified, inputting the target characteristic information into a trained target classical neural network, and outputting a target parameter value corresponding to the target handwritten number as a parameter value of a quantum logic gate;
determining a target quantum neural network according to the parameter value of the quantum logic gate, and calculating a sub-quantum state psi through a quantum circuit corresponding to the target quantum neural network i Corresponding probability C i ；
According to the sub-quantum state psi i Corresponding probability C i And determining a decimal value corresponding to the target handwritten number as a recognition result of the picture to be recognized, and displaying the recognition result in a result display area of the terminal interface.
The handwriting digital recognition method as described above, preferably, further includes, after the step of receiving a digital input command triggered by a user operation:
acquiring whether the input type of the digital input instruction is a selection input type or a handwriting input type;
if the input type is the selection input type, acquiring a pre-stored digital image, and displaying each digital image in an image area to be selected of the terminal interface;
When receiving a selection instruction triggered by the current user based on the digital images, determining a target digital image in each digital image according to the selection instruction, and displaying the target digital image in a second identification area of the terminal interface as the picture to be identified;
if the input type is the handwriting input type, executing: and acquiring handwriting track data of the current user in a first identification area of a terminal interface, and displaying a target handwriting number corresponding to the handwriting track data in the first identification area to serve as the picture to be identified.
In the above method for recognizing handwritten numbers, preferably, after the step of acquiring handwriting track data of a current user in a first recognition area of a terminal interface and displaying a target handwriting number corresponding to the handwriting track data in the first recognition area when a number input instruction triggered by user operation is received, the method further includes:
when an erasure instruction triggered by user operation is received, an erasure track of the current user in the first identification area is acquired, and handwritten digital data in the erasure track is cleared.
In the above method for recognizing handwritten numbers, preferably, when a number input instruction triggered by a user operation is received, the step of acquiring handwriting track data of a current user in a first recognition area of a terminal interface and displaying a target handwritten number corresponding to the handwriting track data in the first recognition area further includes:
and when a clearing instruction triggered by user operation is received, clearing the handwritten digital data in the first identification area so as to allow the current user to re-handwritten the digital to carry out identification operation.
In the above handwritten numeral recognition method, preferably, the target feature information is a target feature matrix, and the step of obtaining the target feature information of the picture to be recognized specifically includes:
acquiring the picture to be identified, and performing binarization processing on the picture to be identified to obtain a corresponding binary picture;
and carrying out matrixing treatment on each pixel in the binary image to obtain a pixel matrix, and carrying out feature extraction on the pixel matrix to obtain a corresponding feature matrix serving as the target feature matrix.
The handwriting recognition method as described above, preferably, said method is based on said sub-quantum state ψ i Corresponding probability C i The step of determining the decimal value corresponding to the target handwritten number as the recognition result of the picture to be recognized specifically includes:
determining the state psi with the sub-quantum state i Corresponding decimal number x i And calculateAnd taking the picture to be identified as an identification result.
The handwriting recognition method as described above, preferably, said method is based on said sub-quantum state ψ i Corresponding probability C i The step of determining the decimal value corresponding to the target handwritten number as the recognition result of the picture to be recognized specifically includes:
according to the sub-quantum state psi i Corresponding probability C i Determining a sub-quantum state corresponding to the maximum probability as a first sub-quantum state;
and determining a decimal number corresponding to the first sub-quantum state as a recognition result of the picture to be recognized.
Preferably, the handwritten numeral recognition method as described above, before the step of inputting the target feature information into the trained target classical neural network, further includes:
acquiring feature information and a label value M corresponding to each sample picture in a training set, wherein M=0, 1, … and 9;
initializing parameters of each parameter-containing sub logic gate in a quantum circuit of a preset quantum neural network, and determining parameter values of the quantum logic gates corresponding to each tag value M by using a back propagation algorithm;
And determining a network structure layer weight parameter and a bias parameter of the classical neural network by using a back propagation algorithm according to the characteristic information corresponding to each sample picture and the parameter value of the quantum logic gate corresponding to each tag value M, and generating the target classical neural network.
In the above handwritten numeral recognition method, preferably, the initializing parameters of each parameter-containing sub-logic gate in a quantum circuit of a preset quantum neural network, and determining parameter values of the quantum logic gates corresponding to each tag value M by using a back propagation algorithm specifically includes:
initializing a group of random values as current parameter values theta for each tag value M in the training set;
according to the set offset delta, positive offset and negative offset corresponding to the current parameter value theta are respectively determined to be theta+delta and theta-delta;
updating parameters of each parameter-containing sub-logic gate in a quantum circuit of the quantum neural network by using the current parameter value theta, the positive offset theta+delta and the negative offset theta-delta respectively to obtain a corresponding sub-quantum state psi i Corresponding probability C i ；
According to the sub-quantum state psi i Corresponding probability c i Calculating to obtain decimal numbers N, N corresponding to the current parameter value theta, the positive offset theta+delta and the negative offset theta-delta + 、N - ；
Determining the gradient grad as (N) + -N - )(N-M)；
Iterating a current parameter value theta of parameters of the quantum logic gate by using theta= (theta-grad), returning to the step of determining positive offset and negative offset corresponding to the current parameter value theta as theta+delta and theta-delta according to the set offset delta until the iteration times reach the set value;
and determining the current parameter value theta corresponding to each tag value M as the parameter value of the quantum logic gate corresponding to each tag value M.
In the handwriting digital recognition method as described above, preferably, the step of determining the network structure layer weight parameter and the bias parameter of the classical neural network by using the back propagation algorithm according to the feature information corresponding to each sample picture and the parameter value of the quantum logic gate corresponding to each tag value M specifically includes:
initializing a group of random values as a weight parameter W and a bias parameter B of the neural network aiming at the characteristic information corresponding to each sample picture in the training set;
processing the characteristic information of the sample picture by utilizing the classical neural network to obtain a calculated value of parameters of the quantum logic gate;
constructing an error function according to the calculated value of the parameter and the parameter value of the quantum logic gate corresponding to the tag value M;
And iteratively adjusting the weight parameter W and the bias parameter B of the neural network by adopting a back propagation algorithm based on gradient descent according to the error function, and returning to the step of processing the characteristic information of the sample picture by utilizing the classical neural network to obtain the calculated value of the parameter of the quantum logic gate until the iteration times reach a set value.
Yet another embodiment of the present application provides a computer readable storage medium including a stored computer program, wherein the computer program, when executed by a processor, controls a device in which the storage medium resides to perform the method.
Still another embodiment of the present application provides a handwritten numeral recognition apparatus, including:
a memory for storing a computer program;
and a processor for implementing the method when executing the computer program.
Compared with the traditional handwriting digital recognition technical scheme in the prior art, the invention utilizes the quantum circuit combination corresponding to the target quantum neural network to recognize the picture containing the handwriting digital so as to realize the application of quantum calculation in the handwriting digital picture recognition, firstly, the digital triggered by the user operation is received When an instruction is input, acquiring handwriting track data of a current user in a first identification area of a terminal interface, and displaying a target handwriting number corresponding to the handwriting track data in the first identification area as a picture to be identified; when a digital identification instruction is received, acquiring target characteristic information of the picture to be identified, inputting the target characteristic information into a trained target classical neural network, and outputting a target parameter value corresponding to the target handwritten number as a parameter value of a quantum logic gate; determining a target quantum neural network according to the parameter value of the quantum logic gate, and calculating a sub-quantum state psi through a quantum circuit corresponding to the target quantum neural network i Corresponding probability C i The method comprises the steps of carrying out a first treatment on the surface of the According to the sub-quantum state psi i Corresponding probability C i And determining a decimal value corresponding to the target handwritten number as a recognition result of the picture to be recognized, and displaying the recognition result in a result display area of the terminal interface.
Drawings
Fig. 1 is a schematic flow chart of a handwriting digital recognition method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a preset quantum circuit according to an embodiment of the present invention;
Fig. 3 is a flowchart of a training method of a neural network in a handwriting digital recognition method according to an embodiment of the present invention.
Detailed Description
The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
The embodiment of the invention firstly provides a handwriting digital recognition method which can be applied to electronic equipment, such as a computer terminal, in particular to a common computer, a quantum computer and the like to form handwriting digital recognition equipment. That is, in an embodiment, a handwriting recognition device may be a computer device on which software for handwriting recognition is loaded. The handwriting digital recognition device includes an interaction module, at least one processor, and a memory. Alternatively, the above-described computer terminal may further include a transmission means for a communication function, and it will be understood by those skilled in the art that the computer terminal may further include more or less components than those listed above, or have a different configuration from those listed above.
The interaction module may include an input/output device of the device, such as a display screen, a mouse, a keyboard, a touch screen, etc., and is configured to receive an input instruction and provide a configuration interface, for example, a user triggers a software icon for handwriting digital recognition on the computer device, the display screen may provide a functional interface for handwriting digital recognition, and the user may input the configuration instruction on the functional interface through the keyboard or the mouse or the touch screen to perform handwriting digital recognition.
The memory is in communication with the at least one processor, the memory storing instructions executable by the at least one memory, which when executed by the at least one processor, cause the at least one processor to perform a method of recognizing handwritten numbers. The memory is a computer-readable storage medium, which may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located with respect to the processor, the remote memory being readable by a computer via a network connection to the computer terminal. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
A handwritten numeral recognition method according to an embodiment of the present invention is described below with reference to the drawings.
An embodiment of the present invention provides a method for recognizing handwritten numbers, referring to fig. 1, the method includes steps S100 to S400, in which:
s100, when a digital input instruction triggered by user operation is received, handwriting track data of a current user in a first identification area of a terminal interface is obtained, and a target handwriting number corresponding to the handwriting track data is displayed in the first identification area and used as a picture to be identified;
After the step of receiving the digital input instruction triggered by the user operation, the method further comprises the following steps:
acquiring whether the input type of the digital input instruction is a selection input type or a handwriting input type;
if the input type is the selection input type, acquiring a pre-stored digital image, and displaying each digital image in an image area to be selected of the terminal interface;
when receiving a selection instruction triggered by the current user based on the digital images, determining a target digital image in each digital image according to the selection instruction, and displaying the target digital image in a second identification area of the terminal interface as the picture to be identified;
if the input type is the handwriting input type, executing: and acquiring handwriting track data of the current user in a first identification area of a terminal interface, and displaying a target handwriting number corresponding to the handwriting track data in the first identification area to serve as the picture to be identified.
In this embodiment, the present invention provides a handwritten number recognition software, where a user may input a number to be recognized in a terminal interface, where the input number to be recognized includes two input types, that is, the input type of the number input instruction includes a selection input type and a handwriting input type. The handwriting input type is that a user inputs a number to be identified through real-time handwriting, and the selection input type is that the user performs digital identification through a hand-written digital picture to be identified which is pre-displayed on a selection terminal interface. When a data input instruction triggered by the user through the digital operation to be identified, which is input through real-time handwriting, is detected, the handwriting track of the user in the effective area of the current terminal interface is detected in real time, namely handwriting track data of the current user in the first identification area of the terminal interface is obtained, then corresponding target handwriting numbers are rendered and displayed in the first identification area according to the handwriting track data, and the target handwriting numbers are used as pictures to be identified. When the corresponding rendering and displaying the target handwritten number does not meet the recognition condition, if the handwritten number is too large or too small, handwriting of the handwritten number is discontinuous, the handwritten number is ambiguous (like 2 and 3), and the like, the target handwritten number corresponding to the handwriting track data in the first recognition area is cleared, and a reminding message that the handwritten number is not normalized and is requested to be input again is displayed. When the selection input type triggered by the user through the selection input button of the terminal interface is detected, a data image (namely, a pre-stored handwritten digital picture to be identified) stored in the database is obtained, and then each digital image is displayed in a preset image candidate area of the terminal interface so as to be selected by the user in each digital image. Detecting a selection instruction triggered by a hand-written digital picture to be identified, which is currently displayed by a user through a selection terminal interface, determining a target digital image in each digital image when the selection instruction is received, and displaying the target digital image in a preset second identification area of the terminal interface. And taking the target digital image as a picture to be identified, and carrying out digital identification operation.
Further, when the digital input instruction triggered by the user operation is received, the step of acquiring handwriting track data of the current user in a first identification area of the terminal interface and displaying a target handwriting number corresponding to the handwriting track data in the first identification area further includes:
when an erasure instruction triggered by user operation is received, an erasure track of the current user in the first identification area is acquired, and handwritten digital data in the erasure track is cleared.
In this embodiment, in order to facilitate the input operation of the user, improve the user experience, and provide the erasing and emptying functions. The handwriting digital area can comprise a plurality of areas, and if a small number of handwriting tracks in a certain area are wrong, a user can trigger an erasure instruction through an erasure button displayed on a terminal interface. If one area or a plurality of areas have large-area handwriting track errors, a user can trigger a clearing instruction through a clearing button displayed on a terminal interface. Specifically, when an erasure instruction triggered by user operation is received, an erasure track of the current user in the first identification area is acquired, and handwritten digital data in the erasure track is cleared, so that the handwritten digital track which is input currently is corrected. And when a clearing instruction triggered by user operation is received, clearing the handwritten digital data in the first identification area so as to allow the current user to re-handwritten the digital to carry out identification operation.
S200, when a digital identification instruction is received, acquiring target characteristic information of the picture to be identified, inputting the target characteristic information into a trained target classical neural network, and outputting a target parameter value corresponding to the target handwritten number as a parameter value of a quantum logic gate;
in the embodiment of the invention, the target feature information is a feature matrix, wherein the picture to be identified is a picture containing handwriting numbers. The method comprises the steps of obtaining target feature information of a picture to be identified, wherein the feature matrix comprises a current parameter value theta, inputting the feature matrix comprising the current parameter value theta into a trained target classical neural network, and outputting a target parameter value theta corresponding to a target handwriting number as a parameter value of a quantum logic gate, wherein the step of obtaining the target feature information of the picture to be identified comprises the following steps:
s201, acquiring the picture to be identified, and performing binarization processing on the picture to be identified to obtain a corresponding binary picture;
s202, carrying out matrixing treatment on each pixel in the binary image to obtain a pixel matrix, and carrying out feature extraction on the pixel matrix to obtain a corresponding feature matrix serving as the target feature matrix.
In this embodiment, the acquisition of the picture to be identified including the handwritten number may be directly acquired by electrically connecting the identification device with the handwriting input device, for example, a touch device such as a touch pad, a touch screen or a handwriting pad receives a digital stroke obtained by touching the touch device by a user, and the touch device converts the digital stroke into the picture to be identified in a format such as bmp or jpg; alternatively, digital strokes written by a user on paper or other visual interface are converted by a scanner or camera or the like into pictures to be recognized in a format such as bmp or jpg. After receiving the pictures to be identified, the handwritten numbers in the pictures to be identified can be segmented, so that each picture to be identified only comprises one handwritten number to be identified.
And then, after receiving the picture to be identified, the identification device of the handwritten number carries out binarization processing on the picture to be identified so as to obtain a binary picture showing a black-and-white effect. In the binary processing process, the gray threshold of the binary processing may be set by a person skilled in the art, and it is known that the gray threshold may be used to determine a pixel value of each pixel in the binary image in the image to be identified. After the binary processing, the picture to be identified is processed into a binary picture with the width of U pixels and the height of V pixels; wherein, U is a positive integer, and V is a positive integer; the U and V may be equal or unequal, which is not limited in this embodiment.
In some embodiments of the present invention, step S202, the step of performing matrixing processing on the binary image to obtain a feature matrix of the image to be identified specifically includes: firstly, carrying out matrixing treatment on each pixel in the binary image to obtain a pixel matrix; and then, extracting the characteristics of the pixel matrix to obtain a corresponding characteristic matrix serving as the target characteristic matrix. Specifically, the width of the binary image is M pixels, the height is N pixels, and an mxn matrix is obtained according to each pixel in the binary image, where the position of each matrix element in the matrix corresponds to the position of each pixel in the binary image. And then, further processing the pixel matrix, namely, extracting the characteristics of the pixel matrix according to a preset element quantity threshold value and element values of all elements in the pixel matrix to obtain a one-dimensional characteristic matrix of the pixel matrix. In general, in a binary image, only gray values of a plurality of pixel points are not 0, and these gray values which are not 0 can be understood as positions or stroke tracks of strokes of handwriting numbers, so when the binary image is represented by using a pixel matrix, the pixel matrix can be further simplified, elements with element values which are not 0 are selected from elements of the pixel matrix, row and column values of the elements are obtained, and the row and column values are presented in a one-dimensional array form to represent feature matrices of the pixel matrix, wherein, due to the difference of element numbers of one-dimensional feature matrices corresponding to each pixel matrix, in this embodiment, the threshold value of the element number can be set so as to enable the neural network to identify the feature matrices.
S300, determining a target quantum neural network according to the parameter value of the quantum logic gate, and calculating a sub-quantum state psi through a quantum circuit corresponding to the target quantum neural network i Corresponding probability C i ；
In the embodiment of the invention, the quantum circuit corresponding to the target quantum neural network comprises at least 4 quantum bits, each quantum bit is acted with a parameter-containing sub-logic gate, when the quantum circuit is executed, parameters of the parameter-containing sub-logic gate are updated according to the parameter values obtained in the step S200, and then the target quantum neural network is determined according to the updated parameter values of the quantum logic gate.
Exemplary, referring to FIG. 2, the quantum circuit includes q 0 、q 1 、q 2 、q 3 The total number of the quantum bits is 4, and each quantum bit sequentially acts on an H gate and an RY gate, wherein the matrix form of the RY gate comprises a parameter. Presetting qubit q in a quantum circuit 3 q 2 q 1 q 0 Is |0000 in initial state>The probability of each sub-quantum state after the action of the H gate is equal, then the probability of each sub-quantum state is changed after the action of the RY gate, and a preset quantum circuit is constructed in the mode, so that the fast operation and the rotation to any sub-quantum state are facilitated. In addition to RY gates, the parametric sub-logic gates may also employ RX, RZ, U gates. In some embodiments of the present invention, the parameter-containing sub-logic gate may also be directly applied to each qubit in the preset quantum circuit, or a X, Y, Z gate may be used instead of the H gate in the above example. For a specific form of the above quantum logic gate, see table 1 below.
Exemplary, as shown in connection with FIG. 2, note q 0 、q 1 、q 2 、q 3 The parameters of the RY gates acting on are respectively theta q0 、θ q1 、θ q2 、θ q3 After the processing of step S200, [ a ] is obtained 0 ,a 1 ,a 2 ,a 3 ］ T In this step, a is used 0 、a 1 、a 2 、a 3 Updating theta respectively q0 、θ q1 、θ q2 、θ q3 The calculated sub-quantum state psi i and the corresponding probability C can be obtained by executing the preset quantum circuit i It should be noted here that the sub-quantum state ψ i Probability C corresponding to i Is in a sub-quantum state psi i Amplitude c of the corresponding i Square of (C) i =c i 2 。
It will be appreciated by those skilled in the art that a qubit is the fundamental unit of information in quantum computing, with n qubits corresponding to 2 n Sub-quantum state, q 0 、q 1 、…、q n-1 Refers to a qubit with bits from 0 to n-1. In addition, the representation of the sub-quantum state corresponds to q n-1 q n-2 ……q 0 And the corresponding bits from the right to the left are from the low order to the high order.
For example:
the logic state where 1 qubit is located is 2 sub-quantum states ψ 0 Sum phi 1 Superimposed state, ψ 0 Sum phi 1 Respectively |0>And |1>Any logic state in which the 1 qubit is located can be expressed as:
ψ=a|0>+b|1>
wherein, a and b are amplitudes of |0> | and |1>, and a and b are complex forms.
The matrix corresponding to ψ is expressed as:
after measurement, the logic state in which the 1 qubit is located collapses to a fixed sub-quantum state |0 >Or |1>Wherein, collapse to |0>The probability of (a) is a 2 Collapse to |1>The probability of (b) is b 2 ，a 2 +b 2 =1。
The logic state of the 4 qubits is2 4 (i.e., a superposition of 16) sub-quantum states, wherein the 16 sub-quantum states ψ 0 To psi 15 Are respectively |0000>、|0001>、|0010>、|0011>、|0100>、|0101>、|0110>Sum of 0111>、|1000>、|1001>、|1010>、|1011>、|1100>、|1101>、|1110>Sum of%>At this time, any logic state ψ where the 4 qubits are located can be expressed as:
and the matrix corresponding to ψ is expressed as:
wherein each sub-quantum state (or weighing sub-state component) of the 16 sub-quantum states corresponds to an amplitude c 0 To c 15 One of these plural numbers, c 0 To c 15 The subscript of (c) is the decimal value corresponding to the binary of the quantum state to which the amplitude belongs, we will c 0 To c 15 Each of these complex numbers is referred to as an amplitude. After measurement, collapse to the sub-quantum state ψ 0 To psi 15 The probability of (a) is the square of the corresponding amplitude, i.e. collapsing to the sub-quantum state ψ i Probability C of (2) i =c i 2 ，i=0、1、2、……、15。
The process of quantum computation is a process in which different quantum logic gates operate on corresponding qubits in order, where we combine these in order to act on the sequence of quantum logic gates on the corresponding qubits, called a quantum wire. When the unitary matrix is used for representing the quantum logic gates, the quantum circuit performs calculation, namely, the process of sequentially carrying out left multiplication on the initial quantum states by the unitary matrix corresponding to different quantum logic gates.
Some commonly used quantum logic gates and their corresponding matrices are represented in the following table, H represents an Hadamard gate, X represents a bery-X gate (whose corresponding matrix is bery matrix σx), Y represents a bery-Y gate (whose corresponding matrix is bery matrix σy), Z represents a bery-Z gate (whose corresponding matrix is bery matrix σz), RX represents an arbitrary rotating bery-X gate, RY represents an arbitrary rotating bery-Y gate, RZ represents an arbitrary rotating bery-Z gate, and other knowledge about the quantum logic gate is not repeated here.
Table 1 part of commonly used Quantum logic gates
S400, according to the sub-quantum state psi i Corresponding probability C i And determining a decimal value corresponding to the target handwritten number as a recognition result of the picture to be recognized, and displaying the recognition result in a result display area of the terminal interface.
In some embodiments of the invention, the quantum state ψ is according to the quantum state i Corresponding probability C i The step of determining a decimal value corresponding to the target handwritten number as a recognition result of the picture to be recognized comprises the following steps:
s401, determining the sub-quantum state psi i Corresponding decimal number x i Wherein:
ψ 0 =|0000>and psi is equal to 0 Corresponding decimal number x 0 =0；ψ 1 =|0001>And psi is equal to 1 Corresponding decimal number x 1 =1；ψ 2 =|0010>And psi is equal to 2 Corresponding decimal number x 2 =2；ψ 3 =|0011>And psi is equal to 3 Corresponding decimal number x 3 =3；ψ 4 =|0100>And psi is equal to 4 Corresponding decimal number x 4 =4；ψ 5 =|0101>And psi is equal to 5 Corresponding decimal number x 5 =5；ψ 6 =|0110>And psi is equal to 6 Corresponding decimal number x 6 =6；ψ 7 = |0111>And psi is equal to 7 Corresponding decimal number x 7 =7；ψ 8 =|1000>And psi is equal to 8 Corresponding decimal number x 8 =8；ψ 9 =|1001>And (3) withψ 9 Corresponding decimal number x 9 =9；ψ 10 =|1010>And psi is equal to 10 Corresponding decimal number x 10 =10；ψ 11 =|1011>And psi is equal to 11 Corresponding decimal number x 11 =11；ψ 12 =|1100>And psi is equal to 12 Corresponding decimal number x 12 =12；ψ 13 =|1101>And psi is equal to 13 Corresponding decimal number x 13 =13；ψ 14 =|1110>And psi is equal to 14 Corresponding decimal number x 14 =14；ψ 15 =|1111>And psi is equal to 15 Corresponding decimal number x 15 =15。
S402, calculateAnd taking the picture to be identified as an identification result.
In other embodiments of the present invention, the quantum state ψ is according to the sub-quantum state i Corresponding probability C i， The step of determining the decimal value corresponding to the handwritten number as the recognition result of the picture to be recognized comprises the following steps:
s401' according to the sub-quantum state ψ i Corresponding probability C i Determining the sub-quantum state corresponding to the maximum probability as the first sub-quantum state, and for the above-mentioned quantum state containing q 0 、q 1 、q 2 、q 3 In the quantum circuit example of (1), C is determined first 0 、C 1 、…、C 15 Then determining the sub-quantum state corresponding to the maximum Cmax as the first quantum state;
S402', determining a decimal number corresponding to the first sub-quantum state as a recognition result of the picture to be recognized. Determining a corresponding decimal number x according to the sub-quantum state i The procedure is the same as that described above and will not be repeated here.
Therefore, the embodiment of the invention utilizes the quantum circuit corresponding to the target quantum neural network to combine and identify the picture containing the handwritten number so as to realize the application of quantum calculation in the aspect of identifying the handwritten number picture, firstly, the user operation is receivedWhen a triggered number inputs an instruction, acquiring handwriting track data of a current user in a first identification area of a terminal interface, and displaying a target handwriting number corresponding to the handwriting track data in the first identification area as a picture to be identified; when a digital identification instruction is received, acquiring target characteristic information of the picture to be identified, inputting the target characteristic information into a trained target classical neural network, and outputting a target parameter value corresponding to the target handwritten number as a parameter value of a quantum logic gate; determining a target quantum neural network according to the parameter value of the quantum logic gate, and calculating a sub-quantum state psi through a quantum circuit corresponding to the target quantum neural network i Corresponding probability C i The method comprises the steps of carrying out a first treatment on the surface of the According to the sub-quantum state psi i Corresponding probability C i And determining a decimal value corresponding to the target handwritten number as a recognition result of the picture to be recognized, and displaying the recognition result in a result display area of the terminal interface.
Fig. 3 is a flowchart of a training method of a neural network in a handwriting digital recognition method according to an embodiment of the present invention.
Referring to fig. 3, before the step of inputting the target feature information into the trained target classical neural network, the method specifically includes steps S501 to S503, where:
s501, obtaining feature information and a label value M corresponding to each sample picture in a training set, wherein M=0, 1, … and 9;
in this step, the target feature information is a feature matrix, and the manner of obtaining the feature matrix corresponding to each sample picture in the training set may refer to the foregoing, where the training set has a plurality of sample pictures, and each sample picture has a feature matrix and a label value, where the label value corresponds to a handwritten number on the sample picture.
S502, initializing parameters of each parameter-containing sub-logic gate in a quantum circuit of a preset quantum neural network, and determining parameter values of the quantum logic gates corresponding to each tag value M by using a back propagation algorithm;
That is, in step S502, the parameter values of the quantum logic gates corresponding to the tag values of the sample pictures in the preset quantum lines are determined by using the tag values of the sample pictures in the training set through the back propagation algorithm, and as a specific embodiment of step S502, the following is adopted:
for each tag value M in the training set, initializing a set of random values as a current parameter value θ of a quantum logic gate in the preset quantum circuit, where the number of iterations λ=0;
according to the set offset delta, positive offset and negative offset corresponding to the current parameter value theta are respectively determined to be theta+delta and theta-delta;
updating parameters of a quantum logic gate in the preset quantum circuit by using the current parameter value theta, the positive offset theta+delta and the negative offset theta-delta respectively to obtain a corresponding sub-quantum state psi i and a corresponding probability c i ；
According to the sub-quantum state psi and the corresponding probability C i Calculating to obtain decimal numbers N, N corresponding to the current parameter value theta, the positive offset theta+delta and the negative offset theta-delta + 、N - ；
Determining the gradient grad as (N) + -N - )(N-M)；
The value theta of the current parameter is iterated by utilizing theta= (theta-grad), the iteration times lambda = lambda +1 are updated, the step of determining positive offset and negative offset corresponding to the current parameter value theta as theta + delta and theta-delta according to the set offset delta is returned until the iteration times lambda reach the set value;
And determining the parameter value of the quantum logic gate corresponding to each tag value M in the training set, namely determining the parameter value of the quantum logic gate corresponding to each tag value M in the training set by determining the current parameter value theta corresponding to each tag value M in the training set.
Based on the above-mentioned specific implementation steps, the parameter values of the quantum logic gates corresponding to the tag values M of the sample pictures in the training set can be determined, and based on the feature matrix of the sample pictures in the training set and the parameter values of the quantum logic gates corresponding to the tag values M of the sample pictures, the following step S503 is performed.
S503, determining a network structure layer weight parameter and a bias parameter of a classical neural network by using a back propagation algorithm according to the characteristic information corresponding to each sample picture and the parameter value of the quantum logic gate corresponding to each tag value M, and generating the target classical neural network.
That is, in the step S503, the obtained feature matrix corresponding to each sample picture and the parameter value of the quantum logic gate corresponding to the label value of each sample picture determined by the preset quantum circuit are used as training samples of the input neural network, and the neural network is trained by the training samples to determine the weight parameter W and the bias parameter B in the neural network, and it should be noted that the neural network in the step may be a BP neural network or a convolutional neural network, and other parameters involved in the training process, such as learning rate, iteration number, etc., may be preset according to specific application conditions by those skilled in the art, and are not repeated herein. In some embodiments of the present invention, the neural network in this step may be a network model including only an input layer and an hidden layer, the input layer performs computation processing according to the feature information of the picture to be identified and transmits the computation processing result to the hidden layer, the hidden layer performs computation processing on the computation processing result transmitted by the input layer to obtain the parameter value of the quantum logic gate, where the weight parameter and the bias parameter between the input layer and the hidden layer are determined by referring to the foregoing steps, and the obtained vector [ a ] is output via the hidden layer 0 ,a 1 ,a 2 ,a 3 ］ T For updating theta q0 、θ q1 、θ q2 、θ q3 The quantum state and the corresponding probability can be obtained by presetting quantum circuit calculation, and then the recognition result is determined, compared with the mode that an independent output layer is arranged in a traditional handwriting digital recognition neural network model and is connected with an hidden layer, the calculation processing result transmitted by the hidden layer is further calculated and processed, and the recognition result is output.
One embodiment of step S503 is as follows:
for the feature matrix corresponding to each sample picture in the training set, initializing a group of random values as a weight parameter W and a bias parameter B of the neural network, wherein the iteration times mu=0 can be recorded in the step;
processing the feature matrix of the sample picture by utilizing the neural network to obtain a calculated value of parameters of the quantum logic gate;
constructing an error function according to the calculated value of the parameter and the value of the parameter of the quantum logic gate corresponding to the label value M of each sample picture;
and according to the error function, adopting a back propagation algorithm based on gradient descent to iteratively adjust the weight parameter W and the bias parameter B of the neural network, updating the iteration times mu=mu+1, and returning to the step of processing the feature matrix of the sample picture by using the neural network to obtain the calculated value of the parameter of the quantum logic gate until the iteration times mu reach a set value.
It should be noted that the back propagation algorithm is used to solve the partial derivative of the error function with respect to the parameters in the neural network, so as to optimize the error function to obtain the required neural network model. The flow of the back propagation algorithm: first, forward propagation operation is carried out, and the output value of each layer of nodes of the neural network is calculated layer by layer. Then, for each layer of nodes, the residual is calculated, and the residual is a back-to-front derivation process. Next, the partial derivatives of the weight parameters and the bias parameters are calculated and the weight parameters and bias parameters are updated. And finally, repeating the iterative neural network parameters to enable the error function to be converged to a minimum value, and finally solving to obtain the neural network model.
The embodiment also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program is used for controlling a device where the storage medium is located to execute the steps of the handwriting digital recognition method in the embodiment of the invention when the computer program is run by a processor.
Specifically, in the present embodiment, the storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
The present embodiment also provides a handwritten numeral recognition apparatus including:
a memory for storing a computer program;
and the processor is used for realizing the steps of the handwriting digital recognition method according to the embodiment of the invention when executing the computer program.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application. The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the modules is only one logical function division, and there may be other divisions in practice, such as: multiple modules or components may be combined, or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or modules, whether electrically, mechanically, or otherwise.
The modules described above as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules; can be located in one place or distributed to a plurality of network modules; some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated in one processing module, or each module may be separately used as one module, or two or more modules may be integrated in one module; the integrated modules may be implemented in hardware or in hardware plus software functional modules.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read Only Memory (ROM), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the integrated modules described above may be stored in a computer readable storage medium if implemented as software functional modules and sold or used as a stand-alone product. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or portions contributing to the prior art, and may be embodied in the form of a software product stored in a storage medium, including several instructions for causing a device (may be a computer, a server, etc.) implementing the resource change to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.
The foregoing is merely an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (12)
1. A handwritten numeral recognition method, characterized in that the handwritten numeral recognition method comprises:
when a digital input instruction triggered by user operation is received, handwriting track data of a current user in a first identification area of a terminal interface is obtained, and a target handwriting number corresponding to the handwriting track data is displayed in the first identification area and used as a picture to be identified;
when a digital identification instruction is received, acquiring target feature information of the picture to be identified, inputting the target feature information into a trained target classical neural network, outputting a target parameter value corresponding to the target handwriting number as a parameter value of a quantum logic gate, wherein the feature information and a label value M are corresponding to each sample picture in a training set, and the target classical neural network is generated by determining a network structure layer weight parameter and a bias parameter of the classical neural network by using a back propagation algorithm according to each feature information and the parameter value of the quantum logic gate corresponding to each label value M; determining a target quantum neural network according to the parameter value of the quantum logic gate, and calculating a sub-quantum state psi through a quantum circuit corresponding to the target quantum neural network i Corresponding probability C i ；
According to the sub-quantum state psi i Corresponding probability C i And determining a decimal value corresponding to the target handwritten number as a recognition result of the picture to be recognized, and displaying the recognition result in a result display area of the terminal interface.
2. The handwritten numeral recognition method according to claim 1, wherein after the step of receiving a numeral input instruction triggered by a user operation, further comprising:
acquiring whether the input type of the digital input instruction is a selection input type or a handwriting input type;
if the input type is the selection input type, acquiring a pre-stored digital image, and displaying each digital image in an image area to be selected of the terminal interface;
when receiving a selection instruction triggered by the current user based on the digital images, determining a target digital image in each digital image according to the selection instruction, and displaying the target digital image in a second identification area of the terminal interface as the picture to be identified;
if the input type is the handwriting input type, executing: and acquiring handwriting track data of the current user in a first identification area of a terminal interface, and displaying a target handwriting number corresponding to the handwriting track data in the first identification area to serve as the picture to be identified.
3. The method for recognizing handwritten digits according to claim 2, wherein after the step of acquiring handwriting track data of a current user in a first recognition area of a terminal interface and displaying a target handwriting digit corresponding to the handwriting track data in the first recognition area when a digit input instruction triggered by a user operation is received, the method further comprises:
when an erasure instruction triggered by user operation is received, an erasure track of the current user in the first identification area is acquired, and handwritten digital data in the erasure track is cleared.
4. The method for recognizing handwritten digits according to claim 3, wherein after the step of acquiring handwriting track data of a current user in a first recognition area of a terminal interface and displaying a target handwriting digit corresponding to the handwriting track data in the first recognition area when a digit input instruction triggered by a user operation is received, the method further comprises:
and when a clearing instruction triggered by user operation is received, clearing the handwritten digital data in the first identification area so as to allow the current user to re-handwritten the digital to carry out identification operation.
5. The method for recognizing handwritten numbers according to claim 1, wherein the target feature information is a target feature matrix, and the step of acquiring the target feature information of the picture to be recognized specifically includes:
acquiring the picture to be identified, and performing binarization processing on the picture to be identified to obtain a corresponding binary picture;
and carrying out matrixing treatment on each pixel in the binary image to obtain a pixel matrix, and carrying out feature extraction on the pixel matrix to obtain a corresponding feature matrix serving as the target feature matrix.
6. The method of claim 1, wherein the sub-quantum state ψ is based on the sub-quantum state ψ i Corresponding probability C i The step of determining the decimal value corresponding to the target handwritten number as the recognition result of the picture to be recognized specifically includes:
determining the state psi with the sub-quantum state i Corresponding decimal number x i And calculates Sigma x i C i And taking the picture to be identified as an identification result.
7. The method of claim 1, wherein the sub-quantum state ψ is based on the sub-quantum state ψ i Corresponding probabilityC i The step of determining the decimal value corresponding to the target handwritten number as the recognition result of the picture to be recognized specifically includes:
According to the sub-quantum state psi i Corresponding probability C i Determining a sub-quantum state corresponding to the maximum probability as a first sub-quantum state;
and determining a decimal number corresponding to the first sub-quantum state as a recognition result of the picture to be recognized.
8. The handwritten numeral recognition method according to any one of claims 1-7, wherein before said step of inputting said target feature information into a trained target classical neural network, further comprises:
acquiring feature information and a label value M corresponding to each sample picture in a training set, wherein M=0, 1, … and 9;
initializing parameters of each parameter-containing sub-logic gate in a quantum circuit of a preset quantum neural network, and determining parameter values of the quantum logic gates corresponding to each tag value M by using a back propagation algorithm.
9. The handwritten numeral recognition method as recited in claim 8, wherein the step of initializing parameters of each parameter-containing sub-logic gate in a quantum circuit of a preset quantum neural network, and determining parameter values of the quantum logic gates corresponding to each of the tag values M using a back propagation algorithm, specifically comprises:
initializing a group of random values as current parameter values theta for each tag value M in the training set;
According to the set offset delta, positive offset and negative offset corresponding to the current parameter value theta are respectively determined to be theta+delta and theta-delta;
updating parameters of each parameter-containing sub-logic gate in a quantum circuit of the quantum neural network by using the current parameter value theta, the positive offset theta+delta and the negative offset theta-delta respectively to obtain a corresponding sub-quantum state psi i Corresponding probability C i ；
According to the sub-quantum state psi i Corresponding probability c i Calculating to obtain decimal numbers N, N corresponding to the current parameter value theta, the positive offset theta+delta and the negative offset theta-delta + 、N － ；
Determining the gradient grad as (N) + －N － )(N－M)；
Iterating a current parameter value theta of parameters of the quantum logic gate by using theta= (theta-grad), returning to the step of determining positive offset and negative offset corresponding to the current parameter value theta as theta+delta and theta-delta according to the set offset delta until the iteration times reach the set value;
and determining the current parameter value theta corresponding to each tag value M as the parameter value of the quantum logic gate corresponding to each tag value M.
10. The handwritten numeral recognition method as recited in claim 8, wherein said step of determining network structure layer weight parameters and bias parameters of a classical neural network using a back propagation algorithm based on feature information corresponding to each of said sample pictures and parameter values of quantum logic gates corresponding to each of said tag values M comprises:
Initializing a group of random values as a weight parameter W and a bias parameter B of the neural network aiming at the characteristic information corresponding to each sample picture in the training set;
processing the characteristic information of the sample picture by utilizing the classical neural network to obtain a calculated value of parameters of the quantum logic gate;
constructing an error function according to the calculated value of the parameter and the parameter value of the quantum logic gate corresponding to the tag value M;
and iteratively adjusting the weight parameter W and the bias parameter B of the neural network by adopting a back propagation algorithm based on gradient descent according to the error function, and returning to the step of processing the characteristic information of the sample picture by utilizing the classical neural network to obtain the calculated value of the parameter of the quantum logic gate until the iteration times reach a set value.
11. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program, when run by a processor, controls a device in which the storage medium is located to perform the method of any one of claims 1 to 10.
12. A handwritten numeral recognition apparatus, comprising:
A memory for storing a computer program;
a processor for implementing the method according to any one of claims 1 to 10 when executing the computer program.
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