CN111931756B

CN111931756B - Method, computing device, and computer storage medium for generating electronic signatures

Info

Publication number: CN111931756B
Application number: CN202011099800.5A
Authority: CN
Inventors: 吴毛鹏; 章瑞平; 谢春
Original assignee: Shanghai Ehi Auto Services Co ltd; Shanghai Yihi Chengshan Automobile Rental Co ltd
Current assignee: Shanghai Ehi Auto Services Co ltd; Shanghai Yihi Chengshan Automobile Rental Co ltd
Priority date: 2020-10-15
Filing date: 2020-10-15
Publication date: 2021-01-08
Anticipated expiration: 2040-10-15
Also published as: CN111931756A

Abstract

The present disclosure relates to a method, computing device, and computer storage medium for generating an electronic signature. The method comprises the following steps: acquiring a moving path of an input object on an electronic canvas of a terminal device for calculating the total length of the moving path; in response to determining that the total length of the movement path is greater than or equal to a predetermined length threshold, generating signature image data based on the movement path; determining characteristic points of the strokes included in the moving path, wherein the characteristic points at least comprise a starting point, an end point and a turning point of the strokes; acquiring a user identifier associated with the order, so as to extract characteristic information of a corresponding character of the user identifier from a preset character library; calculating the similarity between the strokes included in the moving path and the strokes of the corresponding characters based on the characteristic points and the characteristic information; and in response to determining that the similarity meets a predetermined condition, appending the signed image data to a file about the order. The present disclosure enables remote electronic signature validation to be achieved safely and conveniently.

Description

Method, computing device, and computer storage medium for generating electronic signatures

Technical Field

The present disclosure relates generally to information processing, and in particular, to methods, computing devices, and computer storage media for generating electronic signatures.

Background

Paperless operations are becoming more popular, for example, users generate documents with electronic signatures by handwriting signing with an electronic palette signing tool or an electronic canvas of a terminal device, and then as confirmation documents (e.g., electronic contracts about car rents with electronic signatures) in transactions (e.g., without limitation, car rental services).

Conventional schemes for generating electronic signatures are for example: the method comprises the steps of collecting a touch moving path in the process of a user handwriting signature, then saving the collected moving path into a signature picture and uploading the signature picture to a server after manually confirming the displayed moving path, so as to generate an electronic contract with an electronic signature. In the above-described conventional scheme for generating an electronic signature, once a signature picture is generated, it indicates that the electronic signature is successful. However, in the process of signature, the problems of non-standard signature, no signature in the case of fake signature, too small signature or too poor signature to be recognized, incomplete signature and the like are avoided, so that if manual confirmation on site is not assisted or field personnel are too busy to confirm the signature, the system cannot know whether the signature is included in the signature picture or whether the included signature is standard, so that the problem of invalid signature is easily generated, and further hidden danger is brought to transaction safety. The traditional OCR recognition method is more used for character matching of accurate written fonts (such as print forms), and the handwritten fonts of users are greatly different from the standard print forms, so that the accurate recognition rate of the handwritten fonts is low, the calculation speed is low, and the link of manually confirming the signature is difficult to replace.

In summary, the conventional scheme for generating the electronic signature has the disadvantages that manual assistance confirmation cannot be separated, and the transaction safety hazard exists.

Disclosure of Invention

The present disclosure provides a method, a computing device, and a computer storage medium for generating an electronic signature, which can implement remote electronic signature confirmation, can reduce potential transaction safety hazards even when a signature is not confirmed manually, and improve convenience of transactions.

According to a first aspect of the present disclosure, a method for generating an electronic signature is provided. The method comprises the following steps: acquiring a moving path of an input object on an electronic canvas of a terminal device for calculating the total length of the moving path; in response to determining that the total length of the movement path is greater than or equal to a predetermined length threshold, generating signature image data based on the movement path; determining characteristic points of the strokes included in the moving path, wherein the characteristic points at least comprise a starting point, an end point and a turning point of the strokes; acquiring a user identifier associated with the order, so as to extract characteristic information of a corresponding character of the user identifier from a preset character library; calculating the similarity between the strokes included in the moving path and the strokes of the corresponding characters based on the characteristic points and the characteristic information; and in response to determining that the similarity meets a predetermined condition, confirming that the movement path matches the user identification for appending the signature image data to a file regarding the order.

According to a second aspect of the present invention, there is also provided a computing device comprising: at least one processing unit; at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions when executed by the at least one processing unit, cause the apparatus to perform the method of the first aspect of the disclosure.

According to a third aspect of the present disclosure, there is also provided a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a machine, performs the method of the first aspect of the disclosure.

In some embodiments, calculating the similarity of the strokes included in the movement path and the strokes of the corresponding text based on the feature points and the feature information comprises: determining whether the stroke number included in the moving path is consistent with the stroke number included in the corresponding character; and in response to determining that the number of strokes included in the movement path coincides with the number of strokes included in the corresponding text, calculating a difference between a first angle and a second angle for determining the similarity based on at least the difference, the first angle being an angle of a straight line from a feature point of each stroke included in the movement path to a coordinate point immediately before the feature point with respect to the first direction, the second angle being an angle of a straight line from the feature point of the stroke of the corresponding text to the coordinate point immediately before the feature point with respect to the first direction, the feature point of the stroke of the corresponding text being included in the feature information and including at least a start point, an end point, and a turning point of the stroke of the corresponding text.

In some embodiments, determining the similarity based at least on the difference comprises: taking the difference value of the first angle and the second angle as a positive value to generate a similarity value of each stroke; generating a stroke average point position similarity value based on each stroke similarity value; and generating a text average stroke similarity value based on the accumulated value of each stroke similarity value and the number of strokes included in the moving path.

In some embodiments, generating the stroke mean point similarity value based on each stroke similarity value comprises: comparing the first characteristic point quantity of each stroke included in the moving path with the second characteristic point quantity of the corresponding stroke of the corresponding character so as to determine the minimum coordinate data of the stroke with the minimum difference value between the first characteristic point quantity and the second characteristic point quantity; and generating a stroke mean point location similarity value based on each stroke similarity value and the minimum coordinate data.

In some embodiments, in response to determining that the similarity meets a predetermined condition, appending the signature image data to the first data comprises: determining whether at least one of: the stroke average point similarity value is less than or equal to a first similarity threshold value; the character average stroke similarity value is less than or equal to a second similarity threshold value; in response to determining that at least one of the above is satisfied, the signature image data is appended to the first data.

In some embodiments, the method for generating an electronic signature further comprises: in response to determining that the total length of the movement path is less than the predetermined length threshold, a signal is generated indicating a re-signature.

In some embodiments, the method for generating an electronic signature further comprises: in response to determining that the similarity does not meet the predetermined condition, generating at least one of a signal indicating re-signing and a signal indicating a mismatch in signatures.

In some embodiments, calculating the total length of the movement path comprises. In response to detecting that the input object touches an electronic canvas of the user terminal, monitoring movement of the input object to determine whether a touch event callback is detected; in response to determining that a touch event callback is detected, calculating coordinate change data in a first direction and coordinate change data in a second direction related to the current touch event callback based on the current coordinates and the coordinates when the last touch event callback was detected; calculating the length of a moving path related to the current touch event callback based on the coordinate change data in the first direction and the coordinate change data in the second direction; and in response to confirming that the input object leaves the electronic canvas of the user terminal, accumulating a length of a movement path of the input object associated with the touch event from touching the electronic canvas of the user terminal to leaving the electronic canvas so as to generate a total length of the movement path.

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure.

Drawings

Fig. 1 shows a schematic diagram of a system for implementing a method for generating an electronic signature according to an embodiment of the present disclosure.

Fig. 2 shows a flow diagram of a method for generating an electronic signature according to an embodiment of the present disclosure.

Fig. 3 shows a flow chart of a method for calculating a total length of a movement path according to an embodiment of the present disclosure.

FIG. 4 schematically shows a schematic diagram of a method for calculating similarity of strokes according to an embodiment of the present disclosure.

Fig. 5 shows a flow diagram of a method for generating an electronic signature according to an embodiment of the present disclosure.

FIG. 6 schematically shows a block diagram of an electronic device suitable for use to implement an embodiment of the disclosure.

Like or corresponding reference characters designate like or corresponding parts throughout the several views.

Detailed Description

Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object.

As described in the foregoing, in the conventional scheme for generating an electronic signature, it is necessary to assist on-site manual confirmation to determine whether a signature picture generated based on a touch moving path during user signature meets requirements, so as to avoid problems of non-standard signature, no signature, too small signature or incomplete signature, and the like, and therefore, there are disadvantages that manual confirmation cannot be disengaged, there are potential transaction safety hazards, it is difficult to realize remote electronic signature confirmation, and it is not beneficial to improve the convenience of transaction.

To address, at least in part, one or more of the above problems and other potential problems, example embodiments of the present disclosure propose a scheme for generating an electronic signature. The scheme comprises the following steps: acquiring a moving path of an input object on an electronic canvas of a terminal device for calculating the total length of the moving path; in response to determining that the total length of the movement path is greater than or equal to a predetermined length threshold, generating signature image data based on the movement path; determining characteristic points of the strokes included in the moving path, wherein the characteristic points at least comprise a starting point, an end point and a turning point of the strokes; acquiring a user identifier associated with the order, so as to extract characteristic information of a corresponding character of the user identifier from a preset character library; calculating the similarity between the strokes included in the moving path and the strokes of the corresponding characters based on the characteristic points and the characteristic information; and in response to determining that the similarity meets a predetermined condition, confirming that the movement path matches the user identification for appending the signature image data to a file regarding the order.

In the above-described aspect, by calculating the total length of the acquired movement path and generating the signature image data when the total length of the movement path is greater than or equal to the predetermined length threshold, the present disclosure can avoid generating invalid signature image data in the case where the signature is too small, the signature is incomplete, or the signature is assumed to be mounted. In addition, by determining the feature points of the strokes included in the moving path and extracting the feature information of the corresponding words of the user identification indicated by the order in the predetermined word stock, the present disclosure can acquire the matching object for verifying the electronic signature in the absence of the pre-stored user signature within the system. Furthermore, by calculating the similarity between the strokes of the handwritten word and the strokes of the characters corresponding to the user identifier of the order based on the feature points of the strokes included in the moving path and the extracted feature information and attaching the signature image data to the file related to the order when the similarity meets the predetermined condition, the similarity between the handwritten word and the matching object can be quickly and accurately calculated, and the problems of low operation speed, low recognition rate, high calculation power consumption and the like caused by complete character matching do not occur. Therefore, the remote electronic signature confirmation method and the remote electronic signature confirmation device can realize remote electronic signature confirmation, can reduce the potential safety hazard of transaction under the condition of separating from manual signature confirmation, and improve the convenience of transaction.

Fig. 1 shows a schematic diagram of a system 100 for implementing a method for generating an electronic signature according to an embodiment of the present disclosure. As shown in fig. 1, the system 100 includes: a plurality of terminal devices 110 (including, for example, first terminal device 110-1 through nth terminal device 110-N), a computing device 130, and a network 150. A plurality of terminal devices 110, computing devices 130, may interact with data via network 150.

Regarding the terminal device 110, it is used to collect the moving path of the user input object on the electronic canvas, then provide the collected moving path of the input object to the computing device 130, then calculate the total length of the moving path by the computing device 130, and generate signature image data if it is determined that the total length of the moving path is greater than or equal to the predetermined length threshold, and calculate the similarity of the strokes included in the moving path and the strokes of the corresponding text identified by the user. In some embodiments, the terminal device 110 may be configured to not only capture a movement path of the user input object on the electronic canvas, but the terminal device 110 may locally calculate a total length of the movement path, and if it is determined whether the total length of the movement path is greater than or equal to a predetermined length threshold, generate signature image data based on the movement path, and calculate a similarity of strokes included in the movement path and strokes of a corresponding text. The terminal device 110 is, for example, an electronic signature device or a user terminal (the user terminal is, for example, a mobile phone or a tablet computer of a salesperson, a mobile phone or a tablet computer of a user, or the like). The terminal device 110 is previously created with an electronic canvas for a user to perform handwriting input using an input object. The input object is, for example and without limitation, an input device such as a brush or a finger of a user. The refresh frequency of the terminal device 110 is for example 60 frames/second.

Regarding the computing device 130, it is used to acquire the movement path of the input object on the electronic canvas of the terminal device 110 and calculate the total length of the movement path; confirming whether signature image data is generated based on the moving path; determining characteristic points of strokes included in the moving path, and extracting characteristic information of corresponding characters of order association user identifications from a preset character library; calculating the similarity between the strokes included in the moving path and the strokes of the corresponding characters based on the characteristic points and the characteristic information; and in a case where it is determined that the similarity meets a predetermined condition, attaching the signature image data to a file regarding the order. The computing device 130 is, for example and without limitation, a server, a desktop computer, or the like. The computing device 130 may also have one or more processing units, including special purpose processing units such as image processing units GPU, field programmable gate arrays FPGA, and application specific integrated circuits ASIC, and general purpose processing units such as central processing unit CPU. In some embodiments, computing device 130 includes, for example: a moving path acquiring unit 132, a moving path total length calculating unit 134, a stroke feature point determining unit 136, a user identification feature information extracting unit 138, a similarity calculating unit 140, and a signature image data attaching unit 142.

Regarding the movement path acquiring unit 132, it is used to acquire the movement path of the input object on the electronic canvas of the terminal device for calculating the total length of the movement path.

A total length on moving path calculation unit 134 for determining whether the total length of the moving path is greater than or equal to a predetermined length threshold; and generating signature image data based on the movement path if it is determined that the total length of the movement path is greater than or equal to a predetermined length threshold.

The stroke feature point determining unit 136 is configured to determine feature points of the stroke included in the moving path, where the feature points include at least a start point, an end point, and a turning point of the stroke.

And a user identification feature information extracting unit 138 for acquiring the user identification associated with the order, so as to extract feature information of a corresponding word of the user identification in the predetermined word stock.

And a similarity calculation unit 140 for calculating similarity between the strokes included in the movement path and the strokes of the corresponding text based on the feature points and the feature information.

A signature image data attaching unit 142 for determining whether the similarity satisfies a predetermined condition; and confirming that the movement path matches the user identification if the similarity is determined to meet the predetermined condition, so as to attach the signature image data to a file regarding the order.

A method 200 for generating an electronic signature according to an embodiment of the present disclosure will be described below in conjunction with fig. 2. Fig. 2 shows a flow diagram of a method 200 for generating an electronic signature according to an embodiment of the present disclosure. It should be understood that the method 200 may be performed, for example, at the electronic device 600 depicted in fig. 6. May also be executed at the computing device 130 depicted in fig. 1. Or at the terminal device 110 depicted in fig. 1. The method 200 is described below with respect to the computing device 130 as an example. It should be understood that method 200 may also include additional acts not shown and/or may omit acts shown, as the scope of the disclosure is not limited in this respect.

At step 202, the computing device 130 calculates the similarity between the strokes included in the movement path and the strokes of the corresponding text based on the feature points and the feature information.

With respect to the terminal device, it is, for example, pre-configured to have created an electronic canvas, brush, and path object. Regarding the way the electronic canvas is created, it includes, for example: the electronic canvas properties are first configured. The width, height and orientation of the electronic canvas, for example in pixels, is then set, which for example comprises two modes, one vertical and the other horizontal. Thereafter, the resolution and color mode of the electronic canvas are configured. As for the color mode, it is configured, for example, as an RGB _565 mode. Because creating the electronic canvas involves the creation of a bitmap (bitmap), because the bitmap can occupy a large amount of memory of the terminal equipment in the creation process, by setting the color mode to be the RGB _565 mode, the method and the device can reduce the occupation of the memory of the terminal equipment. For example, setting the color mode to RGB _565 mode may reduce the memory usage of the terminal device by half compared to setting the color mode to ARGB _8888 mode.

With respect to the input object, it is for example, but not limited to, a brush. In some embodiments, the computing device 130 is further configured with an input data processing unit to eliminate jaggies, prevent jitter, etc., for preventing the signature image data generated eventually from being unclear or from being unsmooth due to a brush pen during the user's gesture movement. The computing device 130 also creates a path object for drawing the movement path of the input object as an image during the user gesture movement.

As for the way of calculating the total length of the moving path, it includes, for example: if the computing device 130 detects that the input object touches the electronic canvas of the user terminal, monitoring movement of the input object to determine whether a touch event callback is detected; if the touch event callback is determined to be detected, calculating coordinate change data in a first direction and coordinate change data in a second direction related to the current touch event callback based on the current coordinate and the coordinate when the previous touch event callback is detected; calculating the length of a moving path related to the current touch event callback based on the coordinate change data in the first direction and the coordinate change data in the second direction; if the computing device 130 confirms that the input object leaves the electronic canvas of the user terminal, the length of the movement path of the input object associated with the touch event from the time the input object touches the electronic canvas of the user terminal to the time the input object leaves the electronic canvas is accumulated to generate the total length of the movement path. The method 300 for calculating the total length of the moving path will be described in detail with reference to fig. 3, and will not be described herein again.

At step 204, the computing device 130 determines whether the total length of the movement path is greater than or equal to a predetermined length threshold.

As for the predetermined length threshold, it is, for example, a preset threshold corresponding to the pixel length visible to the human eye. For example, with the pixel density of the terminal device of the android system being 160dpi as a reference standard, the predetermined length threshold thereof is configured to be 50, for example, without limitation. For example, the predetermined length threshold is configured to be scaled up or down according to a comparison of the pixel density of the current terminal device with a pixel density reference standard of 160 dpi. The method for configuring the predetermined length threshold is described below with reference to formula (1).

K=P*50/160 (1)

In the above formula (1), K represents a predetermined length threshold configured for the current terminal device. P represents the pixel density of the current terminal device. For example, for a terminal device with a pixel density of 240dpi, the predetermined length threshold is configured, for example and without limitation, to 240/160 × 50 = 75.

If the computing device 130 determines that the total length of the movement path is less than the predetermined length threshold, then it jumps to step 220 to generate a signal indicative of a re-signature. If the total length of the handwriting movement path is less than the predetermined length threshold, it indicates that the signature belongs to an invalid signature, for example, the signature is too small to be recognized.

At step 206, if the computing device 130 determines that the total length of the movement path is greater than or equal to the predetermined length threshold, signature image data is generated based on the movement path. By comparing the total length of the path of the handwritten movement with a preset threshold of pixels visible to the human eye, if the total length is greater than or equal to the preset threshold of pixels, the signature is recognizable, and a signature image can be generated based on the path of the movement. Thus, the present disclosure can ensure that the generated signature image is easy to distinguish by human eyes.

At step 208, computing device 130 determines feature points of the stroke included in the movement path, the feature points including at least a start point, an end point, and a turning point of the stroke. For example, taking the "seven" word as an example, the feature points acquired by the computing device 130 include the start point, the end point, and the turning point of the stroke.

Regarding the determination manner of the turning point, it includes, for example: the computing device 130 calculates the distance of each point in the current stroke from a line connecting the start point and the end point of the stroke; then, sorting is performed based on the calculated distances; and then, selecting a point farthest from the straight line as a turning point of the current stroke.

At step 210, the computing device 130 obtains a user identification associated with the order for extracting feature information of a corresponding word of the user identification in a predetermined word stock.

As regards the user identification, this is for example the user name.

As for the predetermined word stock, it is, for example, without limitation, a tomoe word stock.

For example, the computing device 130 first obtains a user name in the rental order and then extracts feature information for each word in the predetermined word library that is associated with the user name. The feature information includes, for example, a start point, an end point, and turning point data of each character in the user's name. The following examples illustrate exemplary codes for implementing the extraction of feature information of a corresponding word of a user identification in a predetermined word stock:

“<character>

< utf8 seventy </utf8>

</stroke>

</stroke>

</strokes>

</character>”。

At step 212, the computing device 130 calculates the similarity of the strokes included in the movement path and the strokes of the corresponding text based on the feature points and the feature information.

The manner of calculating the similarity between the strokes included in the movement path and the strokes of the corresponding text may include various ways. For example, the computing device 130 determines whether the stroke number included in the movement path coincides with the stroke number of the corresponding text; if the calculation device 130 determines that the number of strokes included in the movement path coincides with the number of strokes included in the corresponding text, a difference between a first angle and a second angle is calculated for determining the similarity based on at least the difference, the first angle being an angle of a straight line from a feature point of each stroke included in the movement path to a coordinate point immediately before the feature point with respect to the first direction, the second angle being an angle of a straight line from the feature point of the stroke of the corresponding text to the coordinate point immediately before the feature point with respect to the first direction, the feature point of the stroke of the corresponding text being included in the feature information and including at least a start point, an end point, and a turning point of the stroke of the corresponding text.

The method for calculating the first angle or the second angle is described below in conjunction with formula (2).

diretion = atan2(y,x) (2)

In the above equation (2), the repetition represents the calculated first angle or second angle. Y represents a difference in Y-axis coordinates between the feature point and a coordinate point immediately preceding the feature point. X represents a difference in X-axis coordinates between the feature point and a coordinate point immediately preceding the feature point.

In the above means, the similarity is determined by determining the similarity based on the difference between the calculated first angle and second angle, rather than calculating the similarity based on the coordinates of the feature points, mainly because: the coordinates of the feature points are generally related to the size of the electronic drawing board, and when the computing device 130 identifies the movement track of the handwriting input at the terminal device, the size of the electronic canvas at the terminal device may not be obtained in advance, and therefore, a complex data normalization process needs to be performed.

The method 400 for calculating the similarity between the strokes included in the movement path and the strokes of the corresponding text will be described in detail with reference to fig. 4, and will not be described herein again.

At step 214, the computing device 130 determines whether the similarity meets a predetermined condition.

With respect to meeting the predetermined condition, it includes, for example, a first similarity threshold with respect to the stroke mean point similarity value and a second similarity threshold with respect to the entire text mean stroke similarity value. Regarding the calculation manner of the stroke mean point similarity value and the text mean stroke similarity value, the following will be described in detail with reference to fig. 4 and the method 400, and will not be described herein again.

At step 216, if the computing device 130 determines that the similarity meets the predetermined condition, the movement path is confirmed to match the user identification in order to attach the signature image data to the file regarding the order. If the computing device 130 determines that the similarity does not meet the predetermined condition, it jumps to step 218 to generate at least one of a signal indicating a re-signature and a signal indicating a mismatch in signatures.

For example, the computing device 130 first determines whether at least one of the following is satisfied: the stroke average point similarity value is less than or equal to a first similarity threshold value; the text average stroke similarity value is less than or equal to a second similarity threshold. If the computing device 130 determines that at least one of the above is satisfied, the signature image data is appended to the first data.

A method 300 for calculating the total length of the movement path is described below in conjunction with fig. 3. Fig. 3 shows a flow diagram of a method 300 for calculating a total length of a movement path according to an embodiment of the present disclosure. It should be understood that the method 300 may be performed, for example, at the electronic device 600 depicted in fig. 6. May also be executed at the computing device 130 depicted in fig. 1. Or at the terminal device 110 depicted in fig. 1. The method 300 is described below with respect to the computing device 130 as an example. It should be understood that method 300 may also include additional acts not shown and/or may omit acts shown, as the scope of the disclosure is not limited in this respect.

At step 302, the computing device 130 determines whether it has been detected that an input object touches the electronic canvas of the user terminal. For example, the computing device 130 may utilize an internal interface in a gesture listener (getturedetector) to confirm whether "notification of an event that occurs when a tap gesture presses the screen" is detected. The notification is triggered by an ACTION _ DOWN, for example, at a moment when a finger lightly touches the screen, and the related command is, for example, ' ondown ' (motionevent) '. If the computing device 130 determines that no input object is detected touching the electronic canvas of the user terminal, execution continues with step 302.

At step 304, if the computing device 130 determines that an input object is detected to touch the electronic canvas of the user terminal, movement of the input object is monitored to determine whether a touch event callback is detected.

With respect to the method of listening for movement of the input object, this may include, for example, the computing device 130 registering a touch event callback. For example, if the refresh frequency of the terminal device is 60 frames/second. I.e., the refresh time per frame is approximately 16 milliseconds, the computing device 13 may be configured to call back every 16 milliseconds during the gesture movement.

At step 306, if the computing device 130 determines that a touch event callback is detected, coordinate change data in a first direction and coordinate change data in a second direction associated with the current touch event callback are calculated based on the current coordinates and the coordinates at the time the last touch event callback was detected. Based on the current coordinates recalled back for each touch event, the computing device 130 obtains the absolute values of the x-axis and y-axis movement lengths associated with the recall of the single touch event.

At step 308, the computing device 130 calculates a length of the movement path associated with the current touch event callback based on the coordinate change data in the first direction and the coordinate change data in the second direction. For example, after the computing device 130 obtains the absolute values of the x-axis and y-axis movement lengths associated with the single-touch event callbacks, the pathlength of the movement associated with the single-touch event callbacks is calculated by the Pythagorean theorem. Because the path length of the movement associated with the single-touch event callback is too small, the present disclosure may employ floating-point type calculations in order to improve computational accuracy.

At step 310, the computing device 130 confirms whether the input object leaves the electronic canvas of the user terminal. For example, the computing device 130 may utilize an internal interface in a gesture listener (getturedetector) to confirm whether the input object leaves the electronic canvas of the user terminal. For example, the computing device 130 detects a "notification of a lift-off-after-quick-slide event" (which is triggered by, for example, 1 ACTION _ DOWN and a plurality of ACTION _ MOVE and 1 ACTION _ UP acquired by the internal interface, and a related instruction, for example, "notification of an event occurring when a tap gesture presses the screen" is not detected within a predetermined time interval after "onFling (MotionEvent e1, MotionEvent 2, float velocityX, float velocityY)'), to confirm that the input object leaves the electronic canvas of the user terminal.

At step 312, if the computing device 130 confirms that the input object leaves the electronic canvas of the user terminal, the length of the movement path of the input object associated with the touch event from touching the electronic canvas of the user terminal to leaving the electronic canvas is accumulated to generate the total length of the movement path. For example, the computing device 130 may accumulate path lengths for movements associated with call backs per touch event in the movement path according to the gesture, resulting in a total length of the movement path taken from the beginning of the finger touching the electronic canvas to the time the finger left the electronic canvas.

In the scheme, the length of the handwriting movement path of the user can be accurately calculated, so that the method and the device can be used for identifying the situations of invalid signatures such as too small signatures.

A method 400 for calculating stroke similarity is described below in conjunction with FIG. 4. FIG. 4 schematically shows a schematic diagram of a method 400 for calculating similarity of strokes according to an embodiment of the present disclosure. It should be understood that the method 400 may be performed, for example, at the electronic device 600 depicted in fig. 6. May also be executed at the computing device 130 depicted in fig. 1. Or at the terminal device 110 depicted in fig. 1. The method 400 is described below with respect to the computing device 130 as an example. It should be understood that method 400 may also include additional acts not shown and/or may omit acts shown, as the scope of the disclosure is not limited in this respect.

At step 402, the computing device 130 takes the difference between the first angle and the second angle to a positive value to generate each stroke similarity value. The first angle is an angle of a straight line from a characteristic point of each stroke included in the moving path to a coordinate point before the characteristic point relative to the first direction, and the second angle is an angle of a straight line from the characteristic point of the stroke corresponding to the character to the coordinate point before the characteristic point relative to the first direction.

The method for calculating the similarity value of each stroke is described below in conjunction with formula (3).

a+=fabs(diretionA-diretionB) (3)

In the above equation (3), the diagtiona represents the calculated first angle. diremotionb represents the calculated second angle. a + represents an absolute value of a difference between the first angle and the second angle. The absolute value a + of the difference between the first angle and the second angle is added to generate, for example, a similarity value a for each stroke.

At step 404, the computing device 130 generates a stroke mean point similarity value based on each stroke similarity value.

With respect to the manner of generating the stroke mean point similarity value, it includes, for example: comparing the first characteristic point quantity of each stroke included in the moving path with the second characteristic point quantity of the corresponding stroke of the corresponding character so as to determine the minimum coordinate data of the stroke with the minimum difference value between the first characteristic point quantity and the second characteristic point quantity; and generating a stroke mean point location similarity value based on each stroke similarity value and the minimum coordinate data. The method for calculating the stroke mean point similarity value is described below in conjunction with equation (4).

b = a / fmin(pointsA-pointsB) (4)

In the above formula (4), pointsA represents the first feature point number of each stroke included in the movement path. pointsB represents a second number of feature points of corresponding strokes of the corresponding text of the user identification. a represents each stroke similarity value. fmin (pointsA-pointsB) represents the minimum coordinate data of the stroke in which the difference between the first feature point number and the second feature point number is smallest. b represents the stroke mean point similarity value. Generally, the smaller the similarity value of the stroke mean point indicates that each stroke included in the movement path is more similar to the corresponding stroke of the corresponding text.

At step 406, the computing device 130 generates a text average stroke similarity value based on the accumulated value for each stroke similarity value and the number of strokes included in the movement path. Generally, the smaller the average stroke similarity value for a character, the more similar the handwriting included in the movement path is to the corresponding stroke of the corresponding character.

In the scheme, the similarity between the handwritten words and the corresponding words of the order-associated user identification can be quickly and accurately calculated.

A method 500 for generating an electronic signature is described below in conjunction with fig. 5. Fig. 5 shows a flow diagram of a method 500 for generating an electronic signature according to an embodiment of the present disclosure. It should be understood that the method 500 may be performed, for example, at the electronic device 600 depicted in fig. 6. May also be executed at the computing device 130 depicted in fig. 1. Or at the terminal device 110 depicted in fig. 1. Method 500 is described below with respect to terminal device 110 as an example.

At step 502, the terminal device 110 creates an electronic canvas.

At step 504, terminal device 110 creates a brush for handwriting input on an electronic canvas.

At step 506, terminal device 110 listens for brush movement on the electronic canvas for determining a touch event callback and obtaining a brush movement path.

At step 508, terminal device 110 determines whether the total length of the movement path of the brush is greater than or equal to a predetermined length threshold.

At step 510, if the terminal device 110 determines that the total length of the movement path of the brush is less than the predetermined length threshold, a signal indicating re-signing is generated.

At step 512, if the terminal device 110 determines that the total length of the movement path of the brush is greater than or equal to the predetermined length threshold, signature image data is generated based on the movement path.

At step 514, terminal device 110 determines feature points of the stroke included in the movement path, the feature points including at least a start point, an end point, and a turning point of the stroke.

At step 516, the terminal device 110 obtains the user identifier associated with the order, so as to extract the feature information of the corresponding word of the user identifier in the predetermined word stock.

At step 518, terminal device 110 calculates similarity between the strokes included in the movement path and the strokes of the corresponding text based on the feature points and the feature information.

Regarding the way of calculating the similarity of the strokes comprised by the movement path and the strokes of the corresponding text, it may be implemented, for example, based on the KNN algorithm. For example, the terminal device 110 calculates the similarity between the feature point of each stroke included in the handwritten character indicated by the movement path to be recognized and the feature information of each stroke included in the corresponding character identified by the user, using the euclidean distance. A method for calculating the similarity between the strokes included in the movement path and the strokes of the corresponding text based on the KNN algorithm is described below with reference to formula (5).

(5)

In the above-mentioned formula (5),

representing feature vectors in Euclidean space with respect to feature points

And feature vectors regarding feature information

The distance between them.

A feature vector representing feature points of each stroke included in the handwritten text indicated with respect to the movement path.

A feature vector representing feature information of each stroke included in the corresponding text with respect to the user identification.

At step 520, terminal device 110 determines whether the similarity meets a predetermined condition.

At step 522, if the terminal device 110 determines that the similarity meets the predetermined condition, it is confirmed that the movement path matches the user identification so as to attach the signature image data to the file about the order. If the terminal device 110 determines that the similarity does not meet the predetermined condition, it jumps to step 524 to generate at least one of a signal indicating re-signing and a signal indicating mismatch of signatures.

In the scheme, the remote electronic signature confirmation can be realized, the potential safety hazard of transaction can be reduced under the condition of separating from manual signature confirmation, and the transaction convenience is improved.

FIG. 6 schematically illustrates a block diagram of an electronic device (or computing device) 600 suitable for use to implement embodiments of the present disclosure. The device 600 may be a device for implementing the method 200 to 500 shown in fig. 2 to 5. As shown in fig. 6, device 600 includes a Central Processing Unit (CPU) 601 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM, various programs and data required for the operation of the device 600 may also be stored. The CPU, ROM, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

A number of components in device 600 are connected to input/output (I/O) 605, including: an input unit 606, an output unit 607, a storage unit 608, the central processing unit 601 performing the various methods and processes described above, e.g., performing the methods 200-500-e.g., in some embodiments, the methods 200-500 may be implemented as a computer software program stored on a machine readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM and/or the communication unit 609. When the computer program is loaded into RAM and executed by a CPU, one or more of the operations of methods 200-500 described above may be performed. Alternatively, in other embodiments, the CPU may be configured by any other suitable means (e.g., by way of firmware) to perform one or more acts of the methods 200-500.

It should be further appreciated that the present disclosure may be embodied as methods, apparatus, systems, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for carrying out various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the C language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or step diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each step of the flowchart and/or step diagrams, and combinations of steps in the flowchart and/or step diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor in a voice interaction device, a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or step diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or step diagram step or steps.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or step diagram step or steps.

The flowcharts and step diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or step diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two successive method steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each step of the step diagrams and/or flowchart illustration, and combinations of steps in the step diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

The above are merely alternative embodiments of the present disclosure and are not intended to limit the present disclosure, which may be modified and varied by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for generating an electronic signature, comprising:

acquiring a moving path of an input object on an electronic canvas of a terminal device for calculating a total length of the moving path;

in response to determining that a total length of the movement path is greater than or equal to a predetermined length threshold, generating signature image data based on the movement path;

determining characteristic points of the strokes included in the moving path, wherein the characteristic points at least comprise a starting point, an end point and a turning point of the strokes;

acquiring a user identifier associated with an order, so as to extract characteristic information of a corresponding character of the user identifier from a preset character library;

calculating similarity between strokes included in the moving path and strokes of the corresponding characters based on the feature points and the feature information; and

confirming that the movement path matches the user identification in response to determining that the similarity meets a predetermined condition so as to attach the signature image data to a file regarding the order,

wherein calculating the similarity between the strokes included in the movement path and the strokes of the corresponding text based on the feature points and the feature information comprises:

determining whether the number of strokes included in the moving path is consistent with the number of strokes included in the corresponding text; and

in response to determining that the number of strokes included in the movement path coincides with the number of strokes included in the corresponding text, calculating a difference between a first angle, which is an angle of a straight line from a feature point of each stroke included in the movement path to a coordinate point immediately before the feature point, with respect to a first direction, and a second angle, which is an angle of a straight line from a feature point of a stroke of the corresponding text to a coordinate point immediately before the feature point, with respect to the first direction, and including at least a start point, an end point, and a turning point of the stroke of the corresponding text, for determining the similarity based on at least the difference,

wherein determining the similarity based at least on the difference comprises:

taking the difference value of the first angle and the second angle to be a positive value to generate a similarity value of each stroke;

generating a stroke mean point similarity value based on the similarity value of each stroke; and

generating a text average stroke similarity value based on the accumulated value of each stroke similarity value and the number of strokes included in the movement path.

2. The method of claim 1, wherein generating a stroke mean point similarity value based on the each stroke similarity value comprises:

comparing the first characteristic point quantity of each stroke included in the moving path with the second characteristic point quantity of the corresponding stroke of the corresponding character so as to determine the minimum coordinate data of the stroke with the minimum difference value between the first characteristic point quantity and the second characteristic point quantity; and

generating a stroke mean point location similarity value based on the each stroke similarity value and the minimum coordinate data.

3. The method of claim 1 or 2, wherein in response to determining that the similarity meets a predetermined condition, appending the signature image data to a file about the order comprises:

determining whether at least one of:

the stroke average point similarity value is less than or equal to a first similarity threshold value;

the character average stroke similarity value is less than or equal to a second similarity threshold value; and

in response to determining that at least one of the above is satisfied, appending the signature image data to a file about the order.

4. The method of claim 1, further comprising:

generating a signal indicating re-signing in response to determining that the total length of the movement path is less than the predetermined length threshold.

5. The method of claim 1, further comprising:

in response to determining that the similarity does not meet a predetermined condition, generating at least one of a signal indicating re-signing and a signal indicating a mismatch in signatures.

6. The method of claim 1, wherein calculating a total length of the movement path comprises:

in response to detecting that the input object touches an electronic canvas of a user terminal, monitoring movement of the input object to determine whether a touch event callback is detected;

in response to determining that the touch event callback is detected, calculating coordinate change data in a first direction and coordinate change data in a second direction related to the current touch event callback based on the current coordinates and the coordinates when the last touch event callback was detected;

calculating the length of a moving path related to the current touch event callback based on the coordinate change data in the first direction and the coordinate change data in the second direction; and

in response to confirming that the input object leaves the electronic canvas of the user terminal, accumulating a length of a movement path of the input object from touching the electronic canvas of the user terminal to leaving the electronic canvas in relation to a touch event so as to generate a total length of the movement path.

7. A computing device, comprising:

at least one processing unit;

at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions when executed by the at least one processing unit, cause the apparatus to perform the steps of the method of any of claims 1 to 6.

8. A computer-readable storage medium, having stored thereon a computer program which, when executed by a machine, implements the method of any of claims 1-6.