JPH11250114A - Method and device for recognizing construction drawing and computer-readable recording medium recorded with construction drawing recognizing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accurately recognize the contour and framework (walls) of a house drawn in a construction drawing constituted of plural colors. SOLUTION: An image reader 2 reads out an image of a construction drawing constituted of plural colors and a specific color extracting/removing part 13 extracts or removes the image data of a specific color from the read color image data. A number-of-dots measurement array data preparing part 6 measures the number of color dots (black dots in the case of a black-and-white data) or the number of white clots in horizontal and vertical directions from the extracted image data or image data left after removal and prepares horizontal and vertical number-of-dots measurement array data and a contour/ framework recognizing part 7 recognizes the contour and framework of the construction drawing based on the horizontal and vertical number-of-dots measurement array data.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention supports the use of architectural / construction drawings, such as the construction / construction equipment business and the business dealing with fixed assets such as real estate. The present invention relates to a method and an apparatus for recognizing a contour and a skeleton from an architectural / construction drawing in which a function is described, and a computer-readable recording medium in which a control program for realizing the function by a computer is recorded.

[0002]

2. Description of the Related Art In recent years, CAD systems and the like have been used to easily create construction drawings including houses and buildings or piping drawings of water pipes, gas pipes, power / communication cables, etc. Is stored and used in the case of design change or extension / remodeling. However, the data of the construction drawing is not compatible due to the created system, and cannot be used due to elapse of the period or change of the contractor. In addition, when renovating or renovating a house, there are many cases where only old construction drawings are drawn on paper, and all the drawings, including those that do not change the floor plan of the renovation, must be redrawn.

[0003] Therefore, it has been attempted to read a construction drawing drawn on paper and recognize and store the data as data that can be processed by a computer. However, there is no special method or apparatus for that, and the construction drawing is image-scanned. And input the image data to a personal computer or the like, and perform line segment recognition and pattern recognition using a general graphic recognition function. Alternatively, the graphic recognition function can be upgraded by using a high-performance graphic editor to assist, and even if the automatic recognition function is somewhat imperfect, basic vector diagrams such as straight lines and arcs can be converted by raster-to-vector conversion, for example. Some of them can be automatically recognized.

[0004]

However, such a conventional drawing recognition apparatus requires a high degree of operation knowledge and the like, and its use is limited to persons who are accustomed to using a personal computer or the like or a specialized operator, and the construction drawing is not used. It was far from easy to use for those who use it frequently. Although basic diagrams such as straight lines and circular arcs can be automatically recognized, graphic symbols unique to construction drawings (such as walls and columns) composed of combinations of basic diagrams can be individually recognized. Did not. Therefore, when the recognized drawing is corrected, it must be performed for each line segment, which requires much labor.

Further, in the construction drawings of houses and buildings used in the construction industry, even if each line segment on the drawings appears as continuous lines of the same size to the human eye, if they are viewed strictly, they have a thickness of one. The trajectory is not fluctuating. In addition, even if it should be a continuous line, it may be broken in some places. These imperfections occur not only at the time of drawing, but also due to aging of paper, errors in reading, and the like. Therefore, for example, if the limit of the thickness that can be recognized as a line segment is too small, it is recognized that the line segment is broken even with a slight blur.
Also, thick line segments may be recognized as rectangles.

[0006] This is of course not only the quality of the original drawing, but also many drawings with low contrast (the boundary between black and white is not clear) like a so-called blueprint using photosensitive paper.
Furthermore, since the fine points characteristic of the blue printing appear on the surface, it is difficult to perform highly accurate automatic recognition with a conventional drawing recognition device, and the correction requires a lot of trouble, so that it is hardly practical. Was.

By the way, in a house construction drawing (architectural drawing), a wall partitioning a floor plan plays a central role in the drawing, and by recognizing which part in the drawing is a wall, the approximate drawing of the construction drawing is made. I can understand. Therefore, recognizing the position and length of the wall in the drawing is the most important matter in recognizing the construction drawing.

Further, the drawings used in the construction / equipment industry are:
It is often composed of a plurality of colors, such as a plurality of pieces of information being described intentionally by color, or being described or printed on a colored sheet with a pen of a color different from the sheet color. Therefore, even if an attempt is made to recognize the image data of the drawing as a single color shading value or binarized data throughout the drawing,
It is very difficult to distinguish a plurality of pieces of color-coded information.

For this reason, in order to be able to identify color-specific information in a drawing, a color scanner or the like has developed a color drop-out function such as a function using six colors including three primary colors and a mixture thereof. . This color drop-out function can read out information of unnecessary colors.

However, the conventional color scanner has the following problems. (1) Since the color dropout function has only a very limited color reduction function such as three colors or six colors, it is not a color reduction function for a fine level or any color. (2) Since many construction drawings are old drawings, there are many drawings drawn on paper on which grid lines are printed in a color other than the dropout color, and the color dropout function may not be able to cope with them.

SUMMARY OF THE INVENTION The present invention has been made in view of the above points, and has an object to accurately recognize the outline and skeleton of a house or the like from a construction drawing composed of a plurality of colors. An object of the present invention is to make it possible to reliably recognize a contour and a skeleton even if there is a ground color or a grid line. Another object of the present invention is to make it possible to correct the recognition result by positively using a grid line printed on a drawing sheet when the grid line is printed.

[0012]

SUMMARY OF THE INVENTION In order to achieve the above object, the present invention provides a construction drawing recognition method having the following features. Image data of a specific color is extracted from color image image data obtained by reading an image of a construction drawing composed of a plurality of colors, and horizontal and vertical color dots or white dots are extracted from the extracted image data. The number of lines is counted to create horizontal and vertical dot number measurement array data, and the outline and skeleton of the construction drawing are recognized based on the created horizontal and vertical dot number measurement array data.

Alternatively, image data of a specific color is removed from color image image data obtained by reading an image of a construction drawing composed of a plurality of colors, and the image data remaining after the removal is horizontally and vertically removed. The number of color dots or white dots in the direction is measured to create horizontal and vertical dot number measurement array data, and the outline and skeleton of the construction drawing are recognized based on the created horizontal and vertical dot number measurement array data. You may make it.

Further, the color image image obtained by reading the image of the construction drawing is displayed in color, and the partial area of the display image displayed in a specific color is directly specified, thereby specifying the image data to be extracted or removed. Color should be determined.

Further, the range of image data to be recognized is limited based on the outline and skeleton of the construction drawing, and the number of color dots or white dots in the horizontal and vertical directions is measured for each range. Creates a new outline or skeleton by creating vertical dot count measurement array data and recognizing contours and skeletons within each limited range based on the created horizontal and vertical dot count measurement array data. By repeating the process until it becomes impossible, recognition accuracy can be improved.

Further, from the color image image data obtained by reading the image of the construction drawing, image data of a grid line color printed on the ground of the paper of the construction drawing is extracted.
Recognizing the horizontal and vertical straight lines from the extracted image data, measuring the intervals between the straight lines in each direction, obtaining the average value of each interval, recognizing the intervals between the grid lines, Can be modified so that the horizontal and vertical intervals are integer multiples of the recognized grid line intervals.

A construction drawing recognition apparatus according to the present invention has the following means in order to achieve the above object. Image data input means for inputting color image image data obtained by reading an image of a construction drawing composed of a plurality of colors, specific color extraction for extracting image data of a specified color from the color image image data input by the means means,

The number-of-dots measurement array data for generating the number-of-dots measurement data in the horizontal and vertical directions by measuring the number of color dots or white dots in the horizontal and vertical directions for the image data of the specific color extracted by the means. Creating means, contour / skeleton recognizing means for recognizing the outline and skeleton of the construction drawing based on the horizontal and vertical dot number measurement array data created by the means;

Alternatively, instead of the specific color extracting means,
Providing a specific color removing means for removing the specified color image data from the input color image image data, the dot count measurement array data creating means, for the remaining image data after removing the specific color image data, The number of color dots or white dots in the horizontal direction and the vertical direction may be measured to create the horizontal and vertical dot number measurement array data.

Further, a display means for displaying a color image image obtained by reading the image of the construction drawing in color, a specific color designating means for directly designating a partial area displayed in a specific color in the displayed color image image, It is preferable to provide a specific color determining means for determining a display color of the specified partial area as a specific color of the image data to be extracted or removed.

Means for extracting, from the color image image data obtained by reading the image of the construction drawing, image data of the color of a grid line printed on the ground of the paper of the construction drawing; Recognizing the horizontal and vertical straight lines from, measuring the interval between the straight lines in each direction, finding the average value of each interval and recognizing the interval between the grid lines, Drawing recognition result correcting means for correcting the recognition result of the skeleton so that the interval in the horizontal and vertical directions is an integral multiple of the interval of the recognized grid lines can be provided.

The present invention further provides a computer-readable recording medium on which a control program for recognizing the construction drawing by a computer is recorded. The control program is a control program for causing the computer to function as the following units.

Image data input means for inputting color image image data obtained by reading an image of a construction drawing composed of a plurality of colors; extracting image data of a specified color from the color image image data input by the means; Specific color extraction / removal means for removal,

With respect to the specific color image data extracted by the means or the remaining image data from which the specific color image data has been removed, the number of color dots or white dots in the horizontal and vertical directions is measured to determine the horizontal and vertical numbers. Dot number measurement array data creating means for creating direction dot number measurement array data, contour / skeleton recognition for recognizing the outline and skeleton of a construction drawing based on the horizontal and vertical direction dot number measurement array data created by the means means,

[0025]

Embodiments of the present invention will be specifically described below with reference to the drawings. FIG. 1 is a block diagram showing a schematic configuration of an example of a construction drawing recognition apparatus for implementing a construction drawing recognition method according to the present invention, in which a hardware configuration and a function of software processing by a microcomputer are mixedly shown.

This apparatus comprises an overall control unit 1, an image reading unit 2, a communication control unit 3, a memory 4, an automatic skew correction unit 5,
Dot number measurement array data creation unit 6, contour / skeleton recognition unit 7, remapping control unit 8, display unit 9, operation input unit 1
0, an external storage device 11, a printing device 12, a specific color extraction / removal unit 13, and a bus 14 connecting these components. It should be noted that interface units required between these units (or devices) and the bus 14 are not shown.

The overall control unit 1 includes a microcomputer (C) for controlling the operation and functions of the entire construction drawing recognition apparatus.
An automatic skew correction unit 5, a dot number measurement array data creation unit 6, a contour / skeleton recognition unit 7, and the like. , The remapping control unit 8 and the specific color extraction / removal unit 13 can also be realized by software processing of the CPU. The external storage device 11 also serves as a reading device for a recording medium on which a control program executed by the overall control unit 1 is recorded.

The image reading section 2 is an image data input means for scanning a construction drawing such as an architectural drawing composed of a plurality of colors, reading the image, and inputting color image data. A known color image scanner comprising a scanning optical system, a plurality of color filters, an image sensor such as a CCD for each color, and respective driving circuits thereof. It also includes a circuit for binarizing the read image image data at a predetermined resolution to obtain white dot and color dot image data.

The communication control section 3 receives image data or an encoded image data obtained by encoding the run length of the image data from an external device and receives the input image data, instead of taking in the image data from the image reading section 2. In addition to the means, the outline and skeleton data of the construction drawing recognized by this device can be transmitted to an external device. Specifically, it includes a FAX modem and personal computer communication control means.

The memory 4 stores image image data read by the image reading unit 2, image image data or coded image data received by the communication control unit 3,
The image data skew-corrected by the automatic skew correction unit 5, the dot number measurement array data created by the dot number measurement array data creation unit 6, the outline and skeleton recognition results recognized by the outline / skeleton recognition unit 7, and the This is a large-capacity RAM or a hard disk memory for storing image data and the like remapped by the mapping control unit 8.

The automatic skew correction section 5 adjusts the angle of the image data stored in the memory 4 to correct the horizontal and vertical line segments so as to match the horizontal and vertical reference directions of the apparatus. Yes, a known automatic skew correction technique can be used. The image data corrected by the automatic skew correction unit 5 is stored in the memory 4 again.

When a specific color of the color image image data of the construction drawing read by the image reading unit and stored in the memory 4 is designated, the specific color extracting / removing unit 13 extracts or removes the data of the specific color. Then, the extracted image data of the specific color or the remaining image data from which the image data has been removed is stored in the memory 4 again. That is, it is means for extracting / removing data of a specific color from the read color image image data prior to drawing recognition or other processing, the details of which will be described later.

The number-of-dots-measuring-array-data creating section 6 converts the image data or the coded image data stored in the memory 4 after the automatic skew correction, and the image data re-mapped by the re-mapping control section 8 described later. On the other hand, the image data is limited to two directions of the horizontal and vertical directions, and the number of color dots (black dots in the case of black and white) or the number of white dots is measured (counted) for each dot width unit.
Then, it is a dot number measurement array data creating means for creating dot number measurement array data (histogram) in the horizontal and vertical directions based on the result and storing it in the memory 4.

If the read construction drawing is a positive drawing (a drawing whose brightness is lower than the brightness of the ground), the number of color (or black) dots is measured, and a negative drawing (the brightness of the drawing is lower than the brightness of the ground). In the case of (high drawing), the number of white dots is measured.

The outline / skeleton recognition unit 7 recognizes the outline and the skeleton of the construction drawing based on the horizontal and vertical dot number measurement array data created by the dot number measurement array data creation unit 6, and particularly recognizes the wall. Outline / skeleton recognition means for extracting wall data such as position, length, thickness, type, etc.
The details will be described later.

If it is difficult or uncertain to recognize (extract) the wall from the referenced dot number measurement array data,
A request for resetting the creation range of the number-of-dots measurement array data in the horizontal and vertical directions of the drawing is sent to the overall control unit 1, the creation range is changed and the number-of-dots measurement array data creation unit 6 creates the number-of-dots measurement array data again. Let it.

The remapping control unit 8 limits the range of the construction drawing by the part recognized as a wall by the contour / skeleton recognition unit 7 and remaps the part after considering other recognition information. Then, image data for causing the dot number measurement array data creation unit 6 to create dot number measurement array data again for each of the limited ranges is created.
At this time, image data is created in which noise of the original or noise read by the image reading unit 2 is also removed. This data is also stored in the memory 4.

The display unit 9 is input from the image reading unit 2 or the communication control unit 3 and is created by the color image data of the construction drawing and the dot number measurement array data creating unit 6 skew-corrected by the automatic skew correcting unit 5. Horizontal or vertical black or white dot number measurement array data, contour / skeleton recognition unit 7
It is for displaying the wall data recognized by the image data, the image data re-mapped by the re-mapping control unit 8, and the like, and is, for example, a display device used in a color CRT or a color liquid crystal display.

FIGS. 2 and 3 show examples of the display state of the screen 9a of the display unit 9. FIG.
7 is a display example of image data (recognition result) obtained by remapping data of a contour and a skeleton (in this example, a wall) of a construction drawing recognized by the remapping control unit 8. In this display example, double solid lines indicate both sides of the wall, and thin lines indicate the core line of the wall and its extension.

FIG. 3 shows the image data of the construction drawing (input image data) skew-corrected by the automatic skew correction unit 5 and the image data of the wall which is the remapped recognition result simultaneously superimposed and displayed. An example is shown below. In this case, to make it easy to identify both,
The skew-corrected image image data of the construction drawing is displayed in halftone (in FIG. 3, it is indicated by stippling for convenience of illustration), and the image data of the wall as the recognition result is displayed in solid lines.

Further, since the display section 9 is a color display device, the discriminability can be improved by displaying both images in different colors. For example, by displaying the skew-corrected input image image data in light blue and displaying the recognition result image data in orange or green, the operator can easily identify the recognition result portion.

Alternatively, the screen 9a of the display unit 9 may be divided, and the skew-corrected input image data and the image data of the recognition result may be displayed in comparison with each of the divided screens. Alternatively, the skew-corrected input image data and the remapped image data of the recognition result may be selectively displayed on the same screen. In that case, the operation input unit 1 described later
0 may be provided with display selection means (keys or the like).

Further, the display section 9 displays the input color image image and allows the mouse cursor to designate a partial area of the display image which is displayed in a specific color, thereby allowing a specific color to be extracted or removed to be specified. It also functions as a means for direct designation. Further, a screen for the operator to confirm the recognition result is also displayed. That is,
The image data of the re-mapped recognition result is displayed, and the message "Is this recognition result OK? (YES / N
O) ". This allows the operator to confirm whether the wall has been correctly recognized.

The operation input unit 10 is for inputting various operation instructions, function selection commands, edit data, and the like, and is a keyboard, a mouse, a touch panel, or the like. The operation input unit 10 also has a function as a display selection unit,
The display state of the display unit 9 can be changed to a display state desired by the operator. For example, by key operation, the input image data of the skew-corrected construction drawing and the image data of the re-mapped recognition result can be superimposed and displayed, or only one of them can be selected and displayed.

Further, in response to the display "Is this recognition result OK? (YES / NO)", the operation input unit 10 inputs a key or the like for inputting information of "YES" or "NO". It also has a means. Then, if "YES" is selected, the recognition process ends, and if "NO" is selected, the process proceeds to the re-recognition process or the correction process. Thereby, the operator can confirm the content of the recognition result and determine it.

The external storage device 11 stores the input image data, the black dot number measurement array data created by the dot number measurement array data creation section 6, the wall data recognized by the contour / skeleton recognition section 7, and the remapping control. This is a storage device for recording image data or the like of the recognition result re-mapped by the unit 8 on a storage medium that can be taken out, such as a floppy disk (FD) or a magneto-optical disk (OMD). The external storage device 11 is also provided with a floppy disk (FD) or a CDROM in which a control program executed by the CPU in the overall control unit 1 by the microcomputer is recorded.
And the like also serves as a device for reading a recording medium.

The printing device 12 is a printer or a plotter that prints or renders the above-described various data on paper and outputs the data. Here, the definitions of “outline” and “skeleton” and “outer wall” and “inner wall” in construction drawings (mainly architectural drawings) will be described with reference to Tables 1, 2 and 4.

[0048]

[Table 1]

[0049]

[Table 2]

The "outline" means the "outside outline", and only the portion facing the outside of the outer peripheral wall as indicated by a circle in Table 1 (the double line shown in FIG. Case 1 which means the outer line part) and the entire wall (FIG. 4)
(B) is a case indicated by a bold line).

"Skeleton" means all of the walls (FIG. 4 (b)
(C) both cases indicated by bold lines)
There are two cases, and a case 3 meaning a wall excluding the outer contour (only a portion indicated by a thick line in FIG. 4C). In these definitions, unless otherwise specified, they are treated as the normal meaning of Case 1.

The "outer wall" means only the portion facing the outside of the outer peripheral wall as in the case of the outer contour (see FIG. 4).
Case 4 meaning the portion indicated by the line outside the double line shown in FIG. 4A), and Case 5 meaning the entire wall in contact with the outside (the portion indicated by the thick line in FIG. 4B). There are cases.

The "inner wall" is a wall excluding the outer wall (wall = outer wall + inner wall) in both cases 4 and 5, as shown by a circle in Table 2, whereas in case 4 the case is shown in FIG. 4 (c) and the thick line in FIG. 4 (c), and the case 5 is the thick line in FIG. 4 (c).
Also in these definitions, unless otherwise specified, it is treated as the normal meaning of Case 5.

Next, the procedure for recognizing construction drawings (mainly architectural drawings of houses, buildings, etc.) by the construction drawing recognition apparatus shown in FIG. 1 will be described with reference to the flowcharts of FIGS. 5 to 8 and FIG. In these flowcharts, each step is indicated by "S". In this embodiment, the construction drawing to be recognized is a positive drawing.

FIG. 5 is a flowchart showing a main routine of a recognition process for recognizing a drawing by extracting / removing a specific color from the drawing. First, in step 101, the construction drawing set by the color image scanner as the image reading unit 2 is read as a color image image,
The color image data is input. Instead, the communication control unit 3 may receive and input color image data of an architectural drawing or encoded color image data obtained by encoding the run length of the architectural drawing from outside.

Next, in step 102, a specific color to be extracted / removed is determined, and data of the specific color is extracted or removed from the read color image image data. In this case, the color of the grid line printed on the ground of the paper on which the architectural drawing is described is designated as a specific color, and the same data as the specific color is removed. For the determination of the specific color, for example, the input color image data is displayed in color on the display unit 9 and the color of the image data of the partial area clicked by pointing with the mouse cursor is determined as the specific color to be extracted or removed. it can. That is, the color configuration (RGB) of the image data of the designated partial area
Alternatively, the image data having the same ratio is removed by the color filter function.

Thereafter, the process proceeds to step 103, in which the drawing image recognition is performed on the image image data of the drawing from which the data of the specific color has been extracted or removed (in this case, the remaining removed data).
The processing of the drawing recognition subroutine will be described later in detail.

In step 104, it is determined whether or not a specific color has been extracted or removed from the color image data in order to improve the accuracy of drawing recognition. If the specific color cannot be extracted or removed any more, the series of processes may be terminated as it is. However, in this embodiment, the process of recognizing the grid interval in step 106 and correcting the drawing recognized in step 103 is performed. After performing, the process ends. The processing of the subroutine of step 103 will be described later with reference to FIG.

If it is determined in step 104 that there is room to extract or remove a specific color from the further subdivided region, the process proceeds to step 105. Then, the process returns to step 102 with an area (room) partitioned by the outline or skeleton recognized as a drawing as a new limited area. Then, in step 102, data of a specific color is extracted or removed from the limited area, and the image data of the specific color extracted in step 103 or the remaining image data from which the specific color image data has been removed is extracted. Perform drawing recognition processing.

FIG. 6 is a flowchart showing a subroutine of the drawing recognition process in step 103 of FIG. The details of the drawing recognition process according to this embodiment will be described with reference to the flowchart in FIG. The processing in FIG.
5, the reading of a color image image in step 101 and the extraction / removal of a specific color in step 102 in FIG. 5 correspond to the drawing recognition processing in step 1.

In the following description, the color of the grid line printed on the background of the paper is designated as a specific color from the read color image image data, and the remaining black and white two-color image data of that color is removed. It is assumed that drawing recognition is performed on the valued image data. Conversely, of course, it is also possible to designate black or another color as a specific color, and extract only black or other color image data to be a drawing recognition target.

In this subroutine, first, in step 2, the accuracy of the wall recognition processing by the contour / skeleton recognition unit 7 and the like based on the horizontal and vertical black dot number measurement array data created by the dot number measurement array data creation unit 6 In order to improve the skew, the skew of the image data to be recognized is automatically corrected by the automatic skew correction unit 5.
Then, in step 3, the entirety of the automatically skew-corrected image data is set as a survey target.

In step 4, the nest variable is 0 (initial value: meaning that the whole image is targeted). The “nest variable” indicates a narrowing-down stage when narrowing down the analysis range of the construction drawing from the top down. The larger the value of the nested variable, the deeper the analysis (the more advanced the part).

In step 5, a process for limiting the area to be examined for the wall position is performed. Specifically, the instruction of the investigation area is shown immediately before the process, and here, only the preparation for the subsequent processes of steps 6 to 9 (common use of the interface) is performed. The shape of each survey area is a closed loop diagram (rectangular diagram) formed by horizontal and vertical polygonal lines. At first, since the nest variable is 0, the whole drawing is to be investigated.

In step 6, horizontal black dot number measurement array data is created. In this method, the number of black dots in the horizontal direction is counted (measured) for each one dot width in the vertical direction of the image data, and the counted data is held.
When image data of another color is to be recognized, the number of dots of that color is measured. Then step 7
In step, a wall extraction (recognition) process is performed based on the horizontal black dot number measurement array data created in step 6. The processing procedure will be described later.

In step 8, as in step 6, black dot number measurement array data in the vertical direction is created. That is, the number of black dots in the vertical direction is counted (measured) for each horizontal dot width of the image data, and the counted data is held. In step 9, a wall extraction (recognition) process is performed based on the black dot number measurement array data in the vertical direction.

Then, in step 10, it is determined whether there is a wall candidate for dividing the area. If there is at least one wall candidate that divides the region in the horizontal or vertical direction, the process proceeds to step 11. If there is no such wall candidate, the process proceeds to step 13. For example, a wall candidate Wd that divides a region into a survey target region Sa as shown in FIG.
To determine if is present. In step 11, the nest variable is incremented by 1 and reset. This indicates that a wall is recognized from the current analysis area, and the analysis area is limited to a small area in the newly partitioned current area using the wall.

Then, in step 12, the area to be investigated is subdivided. Specifically, at the core line (center line) of the wall candidate recognized in steps 7 and 9,
For example, as shown in FIG. 9A, the survey target area Sa is defined by core lines indicated by thin lines of the wall candidates W and Wd.
a2 is divided into two. Further, the position of the survey target is set in the first subdivided area (for example, the upper left area).

The analysis within the newly subdivided area group does not necessarily require a special order.
It is conceivable to perform the processing in ascending order of the x and y coordinate values of the area start position. FIG. 9B shows an example of the nest No. of each area subdivided by the wall candidates and the analysis processing order. The solid lines indicate the core lines (center lines) of the wall candidates,.
Indicates a nest number, and small numbers 1 to 8 indicate the processing order.

Thereafter, the flow returns to step 5 to repeat the above processing. If there is no wall candidate to be divided in step 10, the process proceeds to step 13 to determine whether there is any remaining homogeneous nest area (the area of the same nest No. in FIG. 9B). to decide. If there is any remaining, the object to be investigated is advanced to the next area of the homogeneous nest in step 15, and the process returns to step 5.

If there is no remaining homogeneous nest area, the flow advances to step 14 to determine whether the nest variable is 0 or not. If the nest variable is not 0, the nest variable is decremented by 1 at step 16 and the process returns to the processing of the nest area one level higher. At step 13, it is determined whether there is a nest area remaining at that level. If there is, in step 15 the search target is advanced to the next nest area, and the process returns to step 5.

If the nest variable is 0 in step 14,
Proceeding to step 17, the position and size of each wall are determined based on the recognition data of the wall candidates for each area obtained in steps 7 and 9, and the wall recognition data is stored in the memory 4. The storage method will be described later. Then, display, printing, storage and the like of the recognition data are selectively executed in step 18, and the processing of the subroutine is terminated, and the process returns to the main routine of FIG.

Next, based on FIG. 7, step 7 in FIG.
Prior to this, the procedure of the wall extraction (recognition) processing of 9 and 9 will be described. Before that, the horizontal and vertical black dots created from the image data of the architectural drawing (floor floor plan), which is the construction drawing, and the entire area thereof 10 and 11 show specific examples of the number measurement array data. In these figures, (a) is image data of an architectural drawing, (b) is horizontal dot count measurement array data, and (c) is vertical dot count measurement array data. Further, FIG. 12 is an enlarged view of the array data for measuring the number of black dots in the vertical direction shown in FIG.

In the architectural drawing of FIG. 10, the symbol of the wall is represented by both sides of the wall and the core line. In the architectural drawing of FIG. 11, the symbol of the wall is filled (black) within the thickness of the wall.
Is represented by In this black dot number measurement array data, the width of the thinnest black line is one dot width, and the length of each black line is horizontal or vertical for each one dot width of the image data of the architectural drawing shown in FIG. It corresponds to the counted value of the number of black dots in the direction.

As is clear from these figures, most (90% or more) of the line segments constituting the architectural drawing are drawn in the horizontal direction or the vertical direction. Has become. Therefore, a peak appears at the position where the wall exists in the black dot number measurement array data in the horizontal direction and the vertical direction.

When the processing of the flow shown in FIG. 7 is started, first, in step 21, a direction crossing the designated direction (vertical direction if the black dot number measurement array data creation in the horizontal direction is designated, and vertical direction if designated in the vertical direction). If it is horizontal)
Next, it is confirmed how many times the loop of the wall recognition process can be performed at predetermined intervals corresponding to the reference unit length of the construction drawing. here,
This unit length is half a space or 1 meter which is the minimum wall interval in the case of a general house.
cm).

For the black dot number measurement array data in the horizontal direction, a dimension slightly longer than the image width in the vertical direction is defined as the width size L. For the black dot number measurement array data in the vertical direction, the horizontal image A dimension slightly longer than the width is automatically set as the width size L. In addition, the half space size is set to h, and this value is determined in advance by inputting the scale data and half space length of the drawing or by automatic calculation by calculation. L / h is calculated from the width size L and the half space size from h, and the value rounded up to the decimal point is defined as the number of executions n of the large loop of the wall recognition process. The range indicated by h in FIG. 12 is the processing range in one large loop.

Next, in step 22, the initial value (i ← 1) of the count value i of the large loop number counter is performed.
Then, in step 23, the count value i of the number counter exceeds the number of possible large loop executions n (i> n).
Is determined. If so, the analysis of the area is terminated. If not, in step 24, it is positioned at the highest peak (indicated by P in FIG. 12) as the first analysis process within the i-th half, and that point is defined as xp. By performing the analysis processing every half-hour in this way, it is highly likely that the peak value in the analysis value is a part of the wall.

Next, in step 25, it is determined whether or not the height of the peak (maximum value), which is the first condition as an object of the wall, is equal to or longer than a half (h). As a result, if the height of the peak is less than half a minute, the recognition of the wall is unknown, and the process proceeds to step 34 in order to proceed to the analysis of the next half-ahead. When the height of the peak is half or more, the process proceeds to the next analysis step 26. In this step 26, the left and right sides from the position of the highest peak (e.g., 2W width) examine the range of wall thickness (double-sided position: W _1, W ₂ and thickness _{We = | W 1 -W 2 |} )
To narrow down. Here, W means the thickness of the wall, and is used, for example, with the same value as Wmax.

As a method of narrowing down, the peak positions at both ends or one side of the highest peak position having a value of (maximum peak value−min) * rate + min or more are determined by the positions W ₁ ,
Position W ₁ of W ₂ or highest peak position one side of xp wall
There is a method to narrow the position W ₂ of the other surface of the case is.

FIG. 13 is an explanatory diagram of this narrowing-down processing. FIG. 13A shows an example in which positions W ₁ and W _{2 on} both sides of the wall exist on both sides of the highest peak position xp. In this case, the highest peak position xp is estimated as a candidate for the center line position of the wall.

[0082] (b) in FIG. 13 is a position W ₁ of one side of the highest peak position xp wall, an example in which there are position W ₂ of the other side to one side. In this case, it can be estimated that the longer peak position is a candidate for the outer wall position of the outer contour indicated by the double line in FIG. 2 and the shorter peak adjacent thereto is a candidate for the inner wall position. Where rate is the first half of the analysis
(When the value of the nest variable is small), it is made small, and in the second half (when the value of the nest variable is large), it is made large (for example, rater = 0.70 at first). min is the minimum value of the black dot number measurement data within a width of about 4 W widened to the left and right sides 2 W of the position xp of the current maximum peak P as shown in FIG. 13C.

In this manner, based on the result narrowed down in step 26 in FIG. 7, the validity as a wall is confirmed in steps 27 and 28. First, in step 27, it is determined whether or not the wall thickness We exceeds its maximum value Wmax (for example, 30 cm). Proceed to.
If not, the process proceeds to step 28, where it is determined whether or not the wall thickness We is less than a minimum value Wmin (for example, 2.5 cm).

As a result, if the wall thickness We is less than the minimum value Wmin, the process proceeds to step 30; otherwise, the process proceeds to step 29. In step 30, the information is obtained on the assumption that a peak other than a wall (for example, a tatami mat, a window, a sliding door, etc.) has been recognized. In step 29, it is determined from the candidate area of the wall whether or not the condition as a wall is satisfied by a bisecting search method described later. Information such as the positions W ₁ and W ₂ and the thickness We is saved (stored), and the process proceeds to step 32.

If the condition as a wall is not satisfied in step 29, the process proceeds to step 32. In step 32, it is determined whether both side positions (coordinates) W ₁ and W ₂ of the wall are inside the area currently being processed. If not, the process proceeds to step 34.

In step 33, the current processing area is subdivided, and W ₁ ,
W ₂ and We are saved (stored). Also, (W ₁ + W ₂ ) /
2 is the center line position of the wall. In step 34, the count value i of the large loop number counter is incremented by 1 in order to perform the next half-ahead process, and the process returns to step 23 to repeat the above-described processing.

In this way, the analysis processing is performed every half from one end (head element) of the target black dot number measurement array data, and when the analysis is completed up to the opposite end, the analysis of the current range is completed. The two directions of the horizontal direction and the vertical direction are separately analyzed with respect to the black dot number measurement array data, and the next analysis range is limited to a range that can be recognized as a wall in the current analysis. Therefore, the result of analyzing the array data of the number of black dots in the horizontal and vertical directions is combined to perform the round robin classification.

In this embodiment, it is considered that the center line position of the wall is arranged so that the interval between the adjacent wall is an integral multiple of a half unit. Then, a range up to a characteristic peak can be effectively used as analysis data. Conversely,
If no feature corresponding to the wall is found within a certain range to be analyzed, even in both the horizontal and vertical directions, the analysis ends. A single analysis range is initially targeted at the entire drawing, and is then limited to a range delimited by the walls that have been discovered thereafter, making it easier to find the peaks on the walls and to analyze the details in detail. So that

Next, the bisecting search method for judging “whether the condition as a wall is satisfied” in step 29 of FIG. 7 will be described with reference to the flowchart of FIG. When the processing of the flow shown in FIG. 8 is started, first, in step 41, initialization of the halving search method is performed. In other words, the number n of the area division elements for the wall analysis is set to 1, and the smallest No. of the array elements of the division elements for which the wall or the non-wall is undetermined.
Is set to 0, and S is set as a divided array element of the survey area.

[0], E

[0] Substitute the input start and end image addresses for each and set the initial settings.

Thereafter, the flow advances to step 42. If the start address S [k] of the investigation area is equal to the end address E [k], the flow shifts to step 53;
Proceed to 3. In step 43, the target area of the image data is divided, and the original area is divided into two equal parts except for errors of decimal numbers or less.

That is, the start and end image addresses S1 and E1 of the first half area to be divided are set as S1 = S
[K], E1 = (1/2) (S [k] + E [k]),
Start and end image addresses S2, E of the latter half area
2 as S2 = (1/2) (S [k] + E [k]) + 1, E
Let 2 = E [k].

As a result, as shown in FIG. 14, for example, in the image data of the construction drawing, the original area width of the line (indicated by the dashed line) where the wall candidate exists is initially divided into two equal parts at the position of one. I do. Thereafter, until the presence or absence of the wall can be determined, the second time at the position 2 shown in FIG.
The wall inspection of steps 44 and 45 is performed in the subdivided area by repeating the halving such as the fourth time at the fourth and fourth positions. In steps 44 and 45,
It is checked whether or not each of the equally-divided areas P1 and P2 is filled with a wall.

Here, as a result of analyzing the array data of the number of black dots in the designated area (from the start address to the end address), the peak height of the black dots (with the wall thickness kept widening) is specified. The following (1) to (3) are compared with the width (length) of the region.

: When it is 5% or less, v = 0: Judge as non-wall part: When it is 95% or more, v = 1: Judge as wall part: In other cases, v = 2: Neither can be judged FIG.

In step 46, if it is not possible to determine whether or not a wall exists in the first half area P1 divided in step 43 (v = 2), the flow proceeds to step 47; otherwise, the flow proceeds to step 49. Step 47
In, the storage array elements S [k], E [k], and v [k] of the area range before being divided in step 43 are converted into the latter half of the area data (start and end image addresses S2, E2 is replaced with the determination result v2).

In step 48, new storage array elements S [n], E [n] and v [n] are set as new storage array elements in step 4
The first half area data (start and end image addresses S1, E1 and determination result v1) divided by 3 are saved, and the process proceeds to step 51. In step 49, the storage array elements S [k], E [k], and v [k] of the area range before the division in step 43 are replaced with the first half area data (start and end image addresses) divided in step 43. S1 and E1 are replaced with the determination result v1).

In step 50, new storage array elements S [n], E [n], v [n] are set as step 4
The second half area data (start and end image addresses S2, E2 and determination result v2) divided by 3 are saved, and the process proceeds to step 51. In step 51, the variable n indicating the new storage array element No. is incremented by 1 so as to indicate the new storage array element of the area address, and then the process proceeds to step 52.

In step 52, the classification code v in the k element indicating the smallest No. of the divided elements for which the wall portion or the non-wall portion is undetermined is a content for which it is not known whether or not a wall exists (v = 2 In the case of ()), the process returns to step 42 and the process of further subdividing is repeated. Otherwise, go to step 53. In step 53, it is determined that one content that cannot be determined whether or not a wall exists has been resolved, and the index k is incremented by one, and then the process proceeds to step 54.

In step 54, the number n of the area division elements for the wall analysis is equal to k indicating the smallest No. of the array elements of the division elements whose wall or non-wall is undetermined. It is determined whether or not they are equal, and if they are equal, it is determined that the division processing for wall recognition has been completed, and the flow proceeds to step 55.
If not, the process returns to step 52.

In step 55, sorting is performed in the order of the start addresses (ascending order) so that the divided area address data (array) is arranged in the ascending order.
Move to That is, S

[0] to S [n-1], E

[0] to E [n-1], v

The data [0] to v [n-1] are sorted in ascending order using S [] as a key.

In step 56, if the wall portion and the non-wall portion are continuous, the process is reduced (represented in one range), and the process is terminated. That is, continuous v
If the value of [] is 0 or 1, S [], E
[] Compress the data. At this time, if an array element having contents (v = 2) for which it is not known whether or not a wall exists is included, if the element before and after that element indicates a wall, the data is changed to wall data. If it indicates a non-wall, it is changed to non-wall data and processed.

FIG. 16 shows an example of analysis of the wall position in the image data by the above-mentioned halving search processing. In FIG. 16A, a wall image image (shaded portion) W and its investigation target area are indicated by broken lines, and this investigation area is located along the position of the previously recognized wall candidate. Is set.
And S

[0] = 0 is the first start address of the investigation area, E

[0] = 15 is the first end address. The numbers in the middle such as 4, 5, 7 are the start or end address (all are image addresses) of the target area after division.

FIG. 16B shows the start address S, end address E of each target area, the determination result v regarding the presence or absence of a wall, the determination status, the sorting status, In addition, the degeneration processing result is shown together with the value of k. And finally, the image address 5
-12 recognize that a wall exists.

Next, a simple example of wall recognition in a construction drawing will be described with reference to FIG. FIG. 17 shows a nested variable (ne
(st) and the actual state of the wall recognized as the region division state. First, a wall is extracted with the nest variable = 0 as the whole construction drawing to be inspected for the wall position. As a result, it is assumed that the outline of the house in the construction drawing and the positions of the wall candidates in the horizontal and vertical directions can be recognized as indicated by the solid line in FIG. However, among the recognized wall candidates, the actual wall is only the portion shown in (a), but it is not yet known.

Then, next, the nest variable = 1 is set, and
A wall is extracted by limiting the investigation target area for each closed loop area divided by the core line of the recognized wall candidate shown in FIG. As a result, in (B), wall candidates indicated by thick lines are newly recognized in the four investigation target regions indicated by,, and among the previously recognized wall candidates, the actual wall is represented by (a).
It is confirmed that there is only the portion shown in FIG. 2B, and the state of the wall shown in FIG.

Further, by setting the nest variable = 2, the four regions, in which the new wall candidate is recognized in (B), are divided by the newly recognized wall to further limit the region to be investigated. Extract the wall. As a result, a wall candidate indicated by a thick line is newly recognized in the two investigation target areas indicated by (C), and a state of the wall illustrated by (c) is recognized.

Thereafter, the nest variable is set to 3, and the two regions in which the new wall candidate is recognized in (C) are divided by the newly recognized wall, respectively, to further limit the region to be investigated. Extract the wall. As a result, if it is not possible to extract a new wall candidate in any of the divided regions, the investigation of the wall position is thereby terminated,
It is determined that the wall position shown in (c) is the final wall recognition result, and the data is stored in the memory.

As described above, until no new wall candidate is extracted in any of the divided investigation target areas, the investigation target area is subdivided and the wall is extracted. Thereby,
Even a small wall can be reliably recognized, and a portion where a wall actually exists and a portion where no wall exists among wall candidates can be accurately determined.

Here, an example of a concrete construction drawing recognition procedure according to the flowchart of FIG.
This will be described with reference to FIGS. 18 to 20
Is a series of figures, which are divided into three figures for convenience of illustration. S2 to S18 in these figures are shown in FIG.
S2 to S18. In addition, an area division diagram and an actual wall state at each stage are also shown.

In the following description, steps are abbreviated as “S”. Image data is input in S1 of FIG. 18 (S10 of FIG. 5).
1, corresponding to S102), automatic skew correction is performed in S2, the entire drawing is investigated in S3, and the nest variable is set to 0 in S4. In S5, the search target is limited. However, since the nest variable is 0, the whole drawing is also set as the search target.

In steps S6 and S7, the black dot number measurement array data in the horizontal direction is created and the wall is extracted. However, a wall other than the contour cannot be found, and the black dot number measurement array data in the vertical direction is created in S8 and S9. To extract a wall, and find a wall other than the contour at two places. Therefore, YES is obtained in S10, and S11
To set the nest variable to 1, subdivide the area (at each wall position) in S12, return to S5, and limit the investigation target area to the leftmost area.

Then, in S6 and S7, a wall was found in two places. In S8 and S9, no wall was found. However, in S10, the result was YES. The processing is repeatedly executed until the result of the determination in S14 of NO becomes NO and the nesting processing is completed, and wall extraction processing is sequentially performed on three divided areas obtained by dividing the vertically long area on the left side by two newly discovered walls.

In this example, a new wall is not found by this, and the nest variable is decremented by 1 in S16 of FIG. 19 to 1 and a vertically elongated region in the middle is set as a survey target region, and a wall is similarly extracted. In this example, no new wall candidate is found. Therefore, the investigation target area is changed to the right vertically elongated area in S15 and S5, and one wall is found in S6 and S7.

Then, YES is obtained in S10 of FIG.
In step S11, the nesting variable is set to 2 and the "nesting process" is started, and the vertical region on the right is divided by a newly discovered wall, and the wall extraction process for each of the divided regions is sequentially performed. As a result, no new wall is found in any of the divided areas, and S1
At 3 the answer is NO and the nesting process is terminated, and the nest variable is decremented to 1 at S16. Since the area of the nest variable 1 does not remain, the nest variable is further returned to 0, but the area still remains. Absent. Therefore, it is determined that the wall extraction process has been completed, the position and size of each wall are determined based on the recognition data of the wall candidates extracted in S17, and the data is stored in the memory.

Next, information (analysis result data) such as the position and size of the wall corresponding to the outline and skeleton of the construction drawing recognized as described above is stored in the memory 4 and the external storage device 11 shown in FIG. For an example of the content stored on the medium,
This will be described with reference to FIG. In FIG. 21, (A) is the number of nests, (B) is the specific nest information, (C) is the horizontal wall information, (D) is the vertical wall information, (E) is the model of the horizontal wall, (F) ) Indicates a vertical wall model.

The nest number indicates the depth of the nesting of the child nest pointers (parent-child relationship). If no wall is recognized, the nest number = 0. The unique nest information includes a nest No., a NEXT sibling pointer, a child nest pointer, the number of walls (horizontal and vertical directions), and a wall information pointer (horizontal and vertical directions).

The NEXT sibling pointer has the next address of the simultaneous hierarchical nest information. Therefore, the nest number in the unique nest information at the location indicated by the pointer has the same value as that of the processing. The child nest pointer has the address of the leading data of the hierarchy nest information one level lower. Therefore, the nest number in the specific nest information at the location indicated by this pointer is a value obtained by adding +1 to that of the processing.

The horizontal wall information shown in (C) is stored with the address indicated by the horizontal wall information pointer in the unique nest information shown in (B) as the head address. The wall information includes a NEXT pointer indicating the position of the start address of the next horizontal wall information, and the starting point coordinates (x coordinate: a,
y coordinate: b), horizontal (x-direction) size of wall: c, vertical (y-direction) size of wall: wall thickness d. These a ~
By d, the model of the horizontal wall shown in (E) can be stored and reproduced.

Similarly, the address indicated by the vertical wall information pointer in the unique nest information is set as the head address.
The vertical wall information shown in (D) is stored. The wall information includes a NEXT pointer indicating the position of the start address of the next vertical wall information, the starting point coordinates of the wall (x coordinate: e, y coordinate: f), the vertical (y direction) size of the wall: g, the wall Horizontal (x-direction) size: Consists of wall thickness h. With these e to h, the model of the vertical wall shown in (F) can be stored and reproduced.

By the way, FIGS. 10 and 11 show examples in which the black dot number measurement array data in the horizontal direction and the vertical direction is prepared with respect to the image data of an actual architectural drawing by using the entire drawing as an investigation area. In the next step of recognizing a wall candidate based on the black dot count measurement array data, the area of the drawing is divided by the recognized wall candidate, and black in the horizontal and vertical directions based on image data in which the investigation target area is limited. FIGS. 22 to 24 show examples of creating dot number measurement array data.
Shown in

FIGS. 23 and 24 are examples in which the investigation target area is further subdivided than in FIG. In this way, until no new wall candidates are found, the investigation target area is subdivided, and the horizontal and vertical black dot number measurement array data based on the image data is created, and the wall is extracted.

In this embodiment, since the construction drawing of the positive image is to be recognized, the number of black dots in the horizontal and vertical directions of the binarized image data is measured (counted) to obtain a black dot number measurement array data. However, when a construction drawing of a negative image is to be recognized, the number of white dots in the horizontal and vertical directions of the binarized image data is measured (counted) to generate white dot number measurement array data. Then, the recognition of the wall can be performed similarly.

Further, the recognition data relating to the outline and skeleton of the construction drawing recognized as described above can be converted into CAD vector data, and compatibility of the CAD data between different models can be obtained.

Next, step 1 of the main routine of FIG.
A detailed example of the subroutine of the process of recognizing the grid interval by 06 and correcting the recognition drawing will be described with reference to the flowchart of FIG. In the subroutine of FIG. 25, a specific color to be extracted is determined in step 121, and data of the specific color is extracted from the read color image data.

Here, the color of the grid line printed on the ground on the paper on which the construction drawing is drawn is specified by the mouse cursor in the ground partial area of the color image image displayed in the same manner as described above. Then, the color of the image of the partial area is determined as the specific color, and the image data of the specific color (grid line image data) is extracted.

In step 122, horizontal and vertical straight lines are recognized from the extracted data. Next, at step 123, the interval between the recognized horizontal and vertical straight lines is measured, and at step 124 the difference between the maximum value and the minimum value of the interval is determined. At step 125, the difference is set to a predetermined value (the value of the range of error). ) Judge whether or not:

If the difference between the intervals is not smaller than the predetermined value, it may not be a grid line because the variation of the intervals is large, so that it is displayed at step 129 that recognition of the intervals is not possible, and the process returns to the main routine of FIG. I do. In this case, the maximum value and the minimum value of the measured interval are deleted, and the difference between the new maximum value and the minimum value after the deletion is obtained again. In step 125, the difference is set to a predetermined value (the value of the error range). ) It may be determined whether or not the following is true. Thereby, the influence of the data due to the noise component can be reduced.

If the difference is equal to or smaller than the predetermined value in step 125, the average value of the interval is calculated in step 126, and the average value is compared with the recognition drawing in step 127, and the interval between the recognized outline and the skeleton is calculated. The recognized drawing is corrected so as to be an integral multiple of the average value of the intervals (the grid intervals). Then, after the grid line and the corrected recognition drawing are superimposed and displayed, the process returns to the main routine of FIG.

FIG. 26 is a diagram in which a color image scanner reads an architectural drawing in which a floor plan is drawn by hand in black on paper on which grid lines of a specific color (for example, light blue) are printed,
5 is a display example of mapped image data. The grid line is actually one space (1.8 m) both in the horizontal and vertical directions.
Is printed at intervals of 4 divisions or 6 divisions, but for convenience of illustration, a grid line of a half interval is shown in which one is divided into two.

FIG. 27 shows a display example of a floor plan image image from which the data of the specific color has been removed. Further, FIG.
Fig. 8 shows a display example of a closed-loop image that forms the outline and skeleton of a house as a result of drawing recognition from the floor plan image image data from which the data of the specific color has been removed.

FIG. 29 shows the result of correcting the recognized outline of the house and the position of the skeleton so that the horizontal and vertical intervals are integer multiples of the recognized grid line intervals.
An example is shown in which the display is superimposed on a grid line. In this case, the interval between the grid lines is 1/4.

If it is difficult to correct the skew in the opposite direction of the house drawing in the handwritten drawing, the handwritten drawing may be described on a grid sheet, and the skew correction may be performed using the grid of the grid sheet. If it is unavoidable to leave it to the user's manual work, it is preferable to attach a manual for urging the user to create a facing drawing that requires as little skew correction as possible when reading the scanner.

[0133]

As described above, conventionally, a grid line of a specific color (sky blue, yellow, brown, etc.) is printed on a drawing described in a plurality of colors for multipurpose or on the background of paper. It was difficult to recognize the outline and skeleton of a house from a construction drawing composed of a plurality of colors, and the rate of misrecognition was high, but according to the present invention, from such a construction drawing However, the outline and the skeleton (wall) can be accurately recognized. At this time, it is possible to easily designate a special color to be extracted or removed from the input color image image data. It is also possible to recognize the interval between the grid lines on the ground and to correct the interval between the outline and the skeleton as the recognition result of the drawing.

Further, according to the present invention, a handwritten drawing in which a line is broken or a straight line is slightly inclined, a drawing having a relatively low contrast such as a blue print, and a drawing having a lot of noise, which were conventionally difficult to accurately recognize. Even in construction drawings with poorly described or poor image quality, such as drawings with colored memos unnecessary for recognizing the outline and skeleton of a house, or old construction drawings, the outline and skeleton, especially walls, can be easily and accurately recognized. be able to.

[Brief description of the drawings]

FIG. 1 is a block diagram showing the configuration of an embodiment of a construction drawing recognition device for implementing a construction drawing recognition method according to the present invention.

FIG. 2 is a diagram showing a display example of image data of a re-mapped recognition result on a display unit 9 of FIG. 1;

FIG. 3 is a diagram showing an example in which input image data of a construction drawing, which has also been skew-corrected, is superimposed on image data of a recognition result and displayed.

FIG. 4 is a diagram for explaining definitions of an outer contour, an outer wall, an inner wall, and a skeleton in a construction drawing.

FIG. 5 is a flowchart showing a main routine of construction drawing recognition processing by the construction drawing recognition device shown in FIG. 1;

6 is a flowchart showing a subroutine for recognizing a drawing of image data of a drawing from which a specific color has been extracted or removed in step 103 in FIG. 5;

FIG. 7 is a flowchart of a subroutine of extraction (recognition) processing of walls of steps 7 and 9 in FIG. 6;

FIG. 8 is a flowchart for executing a bisection search method for recognizing the position of a wall.

FIG. 9 is a diagram illustrating an example of a wall candidate in a survey target region, a region division by the wall candidate, and a nest N of a region group subdivided by the wall candidate.
o. FIG. 4 is an explanatory diagram showing an example of the order of analysis processing.

FIG. 10 is a diagram showing a specific example of image data of an architectural drawing (floor floor plan) and black dot number measurement array data in the horizontal and vertical directions created from the entire area thereof.

FIG. 11 is a diagram showing another specific example.

FIG. 12 is an enlarged view showing the arrangement data of the black dot number measurement array in the vertical direction shown in FIG.

FIG. 13 is an explanatory diagram of a process of narrowing both sides of a wall to both sides or one side of a peak in step 26 of FIG. 7;

FIG. 14 is an explanatory diagram of an area width dividing process in step 43 of FIG. 8;

FIG. 15 is an explanatory diagram of a judgment based on a wall survey of a target area in steps 44 and 45 in FIG. 8;

16 is an explanatory diagram illustrating an example of analysis of a wall sample (wall candidate) by the halving search process illustrated in FIG. 8;

FIG. 17 is an explanatory diagram of a simple construction drawing wall recognition example according to the present invention.

FIG. 18 is an explanatory diagram showing an example of a specific construction drawing recognition processing procedure according to the flowchart of FIG. 6;

FIG. 19 is an explanatory view continued from FIG. 18;

FIG. 20 is an explanatory view continued from FIG. 19;

FIG. 21 is an explanatory diagram showing an example of the contents of analysis result data stored in a memory;

22 is a diagram illustrating an example of image data in which the investigation target area of the architectural drawing illustrated in FIG. 10 is limited, and an example of black dot number measurement array data in the horizontal and vertical directions based on the image data.

FIG. 23 is a diagram showing an example of image data in which the investigation target area is further limited from FIG. 22, and an example of black dot number measurement array data in the horizontal and vertical directions based on the image data.

24 is a diagram showing an example of image data of another portion in which the investigation target area is further limited from FIG. 22 and black dot count measurement array data in the horizontal and vertical directions based on the image data.

25 is a flowchart showing a subroutine of processing for recognizing a grid interval and correcting a recognition drawing in step 106 of FIG. 5;

FIG. 26 is a diagram showing a display example of color image image data of a floor plan handwritten on paper on which grid lines of a specific color are printed.

FIG. 27 is a diagram showing a display example of image image data to be subjected to drawing recognition after grid lines of a specific color have been removed from the image image data of the floor plan shown in FIG. 26;

28 is a diagram showing a display example of the outline and skeleton of a house as a result of drawing recognition of the image image data of FIG. 27.

FIG. 29 is a diagram showing an example in which the recognition result is corrected based on the recognition result of the grid line interval, and is displayed by being superimposed on the grid line.

[Explanation of symbols]

 1: Overall control unit (CPU) 2: Image reading unit 3: Communication control unit 4: Memory 5: Automatic skew correction unit 6: Dot number measurement array data creation unit 7: Outline / skeleton recognition unit 8: Remapping control unit 9 : Display unit 10: operation input unit 11: external storage device 12: printing device 13: specific color extraction / removal unit 14: bus

Claims (10)

[Claims] An image data of a specific color is extracted from color image image data obtained by reading an image of a construction drawing composed of a plurality of colors, and the extracted image data is extracted in horizontal and vertical directions. Measure the number of color dots or white dots to create horizontal and vertical dot number measurement array data, and recognize the outline and skeleton of the construction drawing based on the created horizontal and vertical dot number measurement array data. A method for recognizing construction drawings. 2. A method for removing image data of a specific color from color image image data obtained by reading an image of a construction drawing composed of a plurality of colors, and removing horizontal and vertical image data remaining after the removal. The number of color dots or white dots in the direction is measured to create horizontal and vertical dot number measurement array data, and the outline and skeleton of the construction drawing are recognized based on the created horizontal and vertical dot number measurement array data. And a construction drawing recognition method. 3. The construction drawing recognition method according to claim 1, wherein a color image image obtained by reading the image of the construction drawing is displayed in color, and a partial area of the display image displayed in a specific color is directly designated. Thereby determining a specific color of the image data to be extracted or removed. 4. The construction drawing recognition method according to claim 1, wherein a range of the image data is limited based on a recognized outline and a skeleton of the construction drawing. The number of color dots or white dots in the horizontal and vertical directions is measured to create horizontal and vertical dot number measurement array data, and each limited range based on the created horizontal and vertical dot number measurement array data Recognizing a contour and a skeleton in the object until a new contour or skeleton cannot be recognized. 5. The construction drawing recognition method according to claim 1, wherein the construction drawing is printed on a ground of a paper of the construction drawing from the color image image data obtained by reading the image of the construction drawing. The image data of the color of the grid line is extracted, horizontal and vertical straight lines are recognized from the extracted image data, the interval between the straight lines in each direction is measured, and the average value of each interval is obtained. Recognizing the grid line intervals and correcting the outline and skeleton recognition results of the construction drawing so that the horizontal and vertical intervals are integer multiples of the recognized grid line intervals. How to recognize construction drawings. 6. An image data input unit for inputting color image image data obtained by reading an image of a construction drawing composed of a plurality of colors, and image data of a specific color designated from the color image image data input by the unit. A specific color extracting means for extracting the number of color dots or white dots in the horizontal direction and the vertical direction with respect to the image data of the specific color extracted by the means, and creating horizontal and vertical dot number measurement array data And a contour / skeleton recognizing means for recognizing the outline and skeleton of the construction drawing based on the horizontal and vertical dot number measurement array data generated by the means. Construction drawing recognition device. 7. Image data input means for inputting color image image data obtained by reading an image of a construction drawing composed of a plurality of colors, and image data of a specific color designated from the color image image data input by the means. A specific color removing unit that removes image data of a specific color by the unit, and counts the number of color dots or white dots in the horizontal and vertical directions with respect to the image data remaining after removing the image data of the specific color. Dot number measurement array data creating means for creating number measurement array data; contour / skeleton recognition means for recognizing the outline and skeleton of the construction drawing based on the horizontal and vertical dot number measurement array data created by the means; A construction drawing recognition device comprising: 8. The construction drawing recognition method according to claim 6, wherein a display means for displaying a color image image obtained by reading the image of the construction drawing in color, and a specification in the color image image displayed by the display means. Specific color specifying means for directly specifying a partial area displayed in color; and specific color determining means for determining a display color of the partial area specified by the specific color specifying means as a specific color of the image data to be extracted or removed. And a construction drawing recognition device. 9. The construction drawing recognition device according to claim 6, wherein the construction drawing recognition image is printed on a construction drawing paper sheet from color image image data obtained by reading the construction drawing image. Means for extracting image data of the color of the grid line, and recognizing straight lines in the horizontal and vertical directions from the image data extracted by the means, measuring the interval between the straight lines in each direction, and averaging the intervals. Means for obtaining a value and recognizing the interval between the grid lines, and recognizing the outline and skeleton of the construction drawing so that the horizontal and vertical intervals are integer multiples of the recognized grid line intervals. A construction drawing recognition device, comprising: a drawing recognition result correcting means for correcting. 10. A recording medium storing a control program for recognizing a construction drawing by a computer, wherein the computer inputs color image image data obtained by reading an image of the construction drawing composed of a plurality of colors. Image data input means; specific color extracting / removing means for extracting or removing specified color image data from color image image data input by the means; specific color image data extracted by the means or the specific color image data For the remaining image data after removing the color image data,
Dot number measurement array data creating means for creating the horizontal and vertical dot number measurement array data by measuring the number of color dots or white dots in the horizontal direction and the vertical direction, and the horizontal and vertical dots created by the means A computer-readable recording medium on which a control program for functioning as a contour / skeleton recognizing means for recognizing a contour and a skeleton of a construction drawing based on number measurement array data is recorded.