JPH10307913A - Construction drawing recognition method and device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To recognize with high accuracy an outline and the frameworks (walls in particular), i.e., the information which are necessary for knowing the room arrangement out of a construction drawing and also to understand the progressing state of the recognition. SOLUTION: An image reading part 2 reads the image of a construction drawing, and a dot number count array data production part 6 counts the black or white dots in both horizontal and vertical directions of the image data and produces the horizontal and vertical dot number count array data. Based on these array data, an outline/framework recognition part 7 recognizes an outline and the frameworks of the construction drawing. Furthermore, a processing progress rate measurement part 13 measures the progress rate of a recognition process and shows this measurement result at a display part 9 in a percentage (%), etc.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and apparatus for recognizing construction drawings widely used in the construction industry such as the construction and equipment industries. The present invention relates to a method for recognizing construction drawings and an apparatus for recognizing a construction drawing, in which a progress ratio can be specified.

[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 a basic diagram such as a straight line or an arc can be automatically recognized, a graphic symbol (for example, a wall or a column) unique to a construction drawing composed of a combination of the basic diagram and the like is individually recognized. I couldn't do that. Therefore, when the recognized drawing is corrected, it must be performed for each line segment, which requires much labor.

Further, in construction drawings of houses, buildings, and the like used in the construction industry, even if each line segment on the drawing appears to the human eye as a continuous line of the same size, if viewed strictly, the thickness is also 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.

[0007] 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 the construction drawing of a house (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.

For an operator who uses the result of the construction drawing recognition process, the drawing recognition execution time is important in estimating and planning work time. However, due to the complexity of the logic during the recognition process, the construction recognition execution time is important. Since the drawing recognition processing time is often considered to be indefinite, the internal progress status (temporal ratio) during the recognition processing has become a specification that cannot be understood by the operator. Therefore, when using the conventional drawing recognition device, the operator estimates the elapsed time of the drawing recognition process from the model case described in the manual or the recognition processing time of similar drawings in the past and recognizes the drawing. The actual situation is that the size and scale of the data are converted before making the judgment.

[0010] Therefore, when the operator performs a work using the recognition result, it is difficult to estimate the recognition time, and it is difficult to estimate the time required for the entire work. further,
Since the status during the recognition process is not displayed on the screen, etc., even if the recognition system causes a problem during the drawing recognition, it is difficult to identify a defective portion, etc.
Sometimes it became difficult.

The present invention has been made in view of the above points, and is an image image obtained by reading a construction drawing drawn on paper with a noise such as a blueprint, a broken line, or a slightly inclined straight line. To enable automatic and accurate recognition of contours and skeletons, especially walls, which are important information for knowing the floor plan of construction drawings from data or encoded image data obtained by encoding the run length. It is an object of the present invention to be able to know a progress rate of a recognition process (a recognized rate with respect to the entire recognition time) in a process of processing.

[0012]

In order to achieve the above object, the present invention provides the following construction drawing recognition method and construction drawing recognition apparatus. The construction drawing recognition method according to the present invention creates horizontal and vertical dot number measurement array data by measuring the number of black and white dots in the horizontal and vertical directions of image image data obtained by reading an image of a construction drawing,
The method is characterized in that the outline and the skeleton of the construction drawing are recognized based on the created dot number measurement array data in both directions, and the progress rate of the recognition process is specified.

Further, the image data to be recognized is subdivided on the basis of the outline and skeleton recognized by the above method, and the image data in the horizontal and vertical directions is divided for each of the divided areas. It is new to measure the number of black or white dots, create the dot number measurement array data in the horizontal and vertical directions again, and recognize the outline and skeleton of the construction drawing based on the created dot number measurement array data in both directions. It is preferable to repeat the process until a complicated contour or skeleton cannot be recognized.

In the series of recognition processes, the ratio (D0; 0≤d0≤D0) of the total number of regions (D0) when the region to be recognized is first subdivided and the number of regions currently recognized (d0; d0≤D0). d0 / D
By 0), it is possible to specify the current progress ratio until the completion of the recognition processing.

Further, the construction drawing recognition apparatus according to the present invention comprises: image data input means for inputting image data obtained by reading an image of a construction drawing; and black and white or vertical images of the image data inputted by the means. A dot number measurement array data creating unit that creates the horizontal and vertical dot number measurement array data by measuring the number of white dots, and a contour of a construction drawing based on the dot number measurement array data in both directions created by the unit. It has a contour / skeleton recognition means for recognizing a skeleton, and means for measuring the progress rate of a series of recognition processes recognized by the means.

The image data input means may be any image reading means for reading an image of a construction drawing and inputting the image data.

Alternatively, the image data input means is a means for inputting encoded image data obtained by encoding a run length of image image data obtained by reading an image of a construction drawing. The number of black or white dots in the horizontal and vertical directions of the original image image data may be measured from the encoded image data to create the horizontal and vertical dot number measurement array data. The image data input means may be image data receiving means for receiving and inputting the encoded image data by communication.

In the construction drawing recognition apparatus described above, the image data to be recognized is subdivided based on the outline and skeleton recognized by the outline and skeleton recognition means, and the image data is divided into the divided areas. The number-of-dots measurement array data is again created by the number-of-dots measurement array data creation means, and the contour / skeleton recognition means is used by the outline / skeleton recognition means based on the created number-of-dots measurement array data in both directions. It is preferable to have a means for repeating the process of recognizing the outline and the skeleton of the construction drawing until the new outline or the skeleton cannot be recognized.

In this case, the means for measuring the progress rate of the recognition processing includes the total number of areas (D0) when the recognition target area is first subdivided and the number of currently recognized areas (d0; 0≤d).
From the ratio (d0 / D0) to (0 ≦ D0), it is possible to measure the current progress ratio until the completion of the recognition processing.

[0020]

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 a general 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 processing progress ratio measuring unit 13, and a bus 14 for connecting these components. It should be noted that interface units required between these units (or devices) and the bus 14 are not shown.

An overall control unit 1 is a microcomputer (C) for controlling the operation and functions of the entire construction drawing recognition apparatus.
PU, ROM, RAM, etc.
PU ”), the automatic skew correction unit 5 and the dot number measurement array data creation unit 6 according to the present invention.
Each function of the contour / skeleton recognition unit 7, the remapping control unit 8, and the processing progress ratio measurement unit 13 can also be realized by software processing of the CPU.

The image reading unit 2 is an image data input unit that scans a set construction drawing such as an architectural drawing, reads the image and inputs image image data, and includes a scanning optical system and an image sensor such as a CCD. This is a known image scanner including the driving circuit and the like. It also includes a circuit for binarizing the read image image data at a predetermined resolution to obtain white dot and black dot image data.

The communication control unit 3 receives image data or an encoded image data obtained by encoding the run length of the image data by external communication, instead of taking in the image data from the image reading unit 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 encoded 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 unit 5 corrects the angle of the image data stored in the memory 4 so as to make the horizontal and vertical line segment directions coincide with 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.

The processing progress ratio measuring unit 13 measures the number of horizontal and vertical dots generated by measuring the number of black and white dots in the horizontal and vertical directions for the entire first drawing by the outline / skeleton recognition unit 7. Based on the outline and skeleton of the construction drawing recognized based on the measurement sequence data, the number of the first subdivided (largely divided) blocks (the number of divided areas) of the image data of the construction drawing is calculated and stored in the memory 4. Using the stored result as the denominator, during the recognition process, the ratio of the number of recognized blocks to the entire recognition process (the total number of the large divided blocks described above) is calculated as a recognition progress ratio, and is stored in the memory 4 again. It is used for displaying the progress. That is, this is a means for calculating the ratio of the progress in the middle of the entire recognition process, the details of which will be described later.

The dot number measurement array data creating unit 6 converts the image data or the encoded image data stored in the memory 4 after the automatic skew correction and the image data re-mapped by the re-mapping control unit 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 black or white dots is measured (counted) for each dot width unit. (Histogram) and stores it in the memory 4.

When the read construction drawing is a positive drawing (a drawing whose brightness is lower than the ground brightness), the number of black dots is measured, and a negative drawing (a drawing whose brightness is higher than the ground brightness) is taken. In this case, the number of white dots is measured.

The outline / skeleton recognition unit 7 recognizes the outline and 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 walls. 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 set, 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 receives the image data of the construction drawing input from the image reading unit 2 or the communication control unit 3 and skew-corrected by the automatic skew correction unit 5 and the horizontal data generated by the dot number measurement array data generation unit 6. And the number of black or white dot count measurement array data in the vertical direction, the wall data recognized by the contour / skeleton recognition unit 7, the image data remapped by the remapping control unit 8, the progress ratio of the recognition process during the recognition process, and the like. For example, a CRT or a 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.

When the display unit 9 is a color display device, the discrimination 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 the respective 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 unit 9 also displays a screen for the operator to confirm the recognition result. That is, the image data of the re-mapped recognition result is displayed, and a display such as "Is this recognition result OK? (YES / NO)" is performed. 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, editing 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, the operation input unit 10 inputs a key or the like for inputting information of "YES" or "NO" in response to the display of "Is this recognition result OK? (YES / 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 input image data, black dot number measurement array data created by the dot number measurement array data creation unit 6, wall data recognized by the outline / skeleton recognition unit 7, remapping control. This is a storage device for storing image data and the like of the recognition result remapped by the unit 8 in a storage medium that can be taken out, such as a floppy disk (FD) or a magneto-optical disk (OMD). The printing device 12 is a printer or a plotter that prints or renders the above various data on paper and outputs the data.

Here, the definitions of "contour" 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.

[0043]

[Table 1]

[0044]

[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 of 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 7 and FIGS. 8 to 26. I do. In FIGS. 5 to 7, 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 processing for recognizing construction drawings and displaying the elapsed time (ratio) during the recognition processing. First, in step 1,
The construction drawing set in the image reading section 2 in FIG. 1 is read, and the binarized image image data is input. Alternatively, data obtained by encoding image image data may be input from the communication control unit 3.

Next, 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 is improved. For this purpose, the input image data is automatically skew-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 3-1, 1 is set to the variable D0,
Further, 0 is substituted for the variable d0 to initialize the variable d0. D
0 represents the number of divided blocks (the number of regions) when the entire region of one drawing is first subdivided (largely divided), and d0 represents the number of recognized divided blocks (regions).

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 position of the wall 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 target area is a rectangular diagram. 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.
Next, in step 7, 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, array data for measuring the number of black dots 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 area in the horizontal or vertical direction, the process proceeds to step 11, and if there is no such wall candidate, the process proceeds to step 12-3. For example, it is determined whether or not there is a wall candidate Wd that divides the area in the investigation area Sa as shown in FIG.

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.
Are divided into regions Sa1 and Sa2 by a core line indicated by a thin line of. Further, the position of the survey target is set in the first subdivided region (for example, the upper left region). Further, if the nest variable is 1 at this point, D0 is set in S12-2.
Into the subdivision number of the area.

A special order is not required for the order of analysis in the newly subdivided region group. For example, the analysis is performed in the order of smaller x and y coordinate values of the region start position. And so on. FIG. 9 shows an example of the nest No. of each area subdivided by the wall candidates and the order of the analysis processing. 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.

If there is no wall candidate in which the region is to be divided in step 10, the process proceeds to step 12-3 to determine whether the nest variable is 1. If the nest variable is not 1, the process proceeds to step 12-5. If the nest variable is 1, in step 12-4, it is considered that one of the large divided blocks has been recognized, and the recognized processing number d0 of the large divided block is increased by +
1 and proceed to step 12-5.

In step 12-5, d0 / D0 is calculated as (elapsed number of recognized blocks up to the present time) / (total number of large divided blocks) as an elapsed ratio during recognition processing.
Is clearly indicated (displayed). Here, d0 is 0 ≦ d0 ≦ D0. Examples of the display are shown in FIGS.

In the display example shown in FIG. 25, d0 / D0 is expressed in percentage (%) as a band graph and 37%
It is indicated by the numerical value of. This clearly indicates that about 37% of the entire recognition processing has been completed. In the display example shown in FIG. 26, the same percentage (%) as shown in FIG.
Is displayed on the image data of the architectural drawing to be recognized.

Thereafter, the process proceeds to step 13 in FIG. 5, and it is determined whether or not there is any remaining of the same nest area (the area of the same nest No. in FIG. 9). If there is, step 1
In 5, the investigation target is advanced to the next area of the same nest, and the process returns to step 5. Then, the above processing is repeatedly performed.

If there is no remaining homogeneous nest area, the routine proceeds to step 14, where it is determined whether the nest variable is 0 or not. If the nest variable is not 0, the nest variable is decremented by one at step 16 and the process returns to the process of determining whether the nest variable at that stage is 1 at step 12-3 as the process of the nest region one level higher. .

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, in step 18, the recognition data is displayed on the display unit 9 as necessary, printed by the printing device 12, or stored in the external storage device 11, and the process is terminated.

Next, the procedure of the wall extraction (recognition) processing in steps 7 and 9 in FIG. 5 will be described with reference to FIG. 6. Prior to that, the construction drawing (floor plan) of the construction drawing will be described. FIGS. 10 and 11 show specific examples of the array data of the number of black dots in the horizontal and vertical directions created from the image data and the entire area thereof. 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, and 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 or vertical direction, and the density of black dots is particularly high in the wall portion. 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. 6 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 array data of the number of black dots 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 array data of the number of black dots in the vertical direction, the horizontal image is used. 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, at 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 manner, it is highly likely that the peak value in the analysis value is one of the walls.

Next, in step 25, it is determined whether or not the height of the peak (maximum value), which is the first condition as the object of the wall, is equal to or longer than half an hour (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 a point. 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 maximum peak position having a value of (maximum peak value−min) × rate + min or more are determined by using the positions W ₁ and W _{2 on} both surfaces of the wall.
Or highest peak position xp is a method to narrow the position W ₂ of the other surface when the position W ₁ of one side of the wall.

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.

FIG. 13B shows an example in which the highest peak position xp is the position W ₁ on _one surface of the wall, and the position W ₂ on the other surface exists on 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.

Here, the rate is set small in the first half (when the value of the nest variable is small) and large in the second half (when the value of the nest variable is large) (for example, rater = 0.
70). min is the minimum value of the black dot number measurement data within a width of about 4 W widened to 2 W on both the left and right sides of the position xp of the maximum peak P of interest as shown in FIG. 13C.

In this way, based on the result narrowed down in step 26 in FIG. 6, 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 position of the center line 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 half-way 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 assumed 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 initially covers the entire drawing, and is then limited to a range demarcated by walls that have been discovered since then, making it easier to find wall peaks and to analyze 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. 6 will be described with reference to the flowchart of FIG. When the processing of the flow shown in FIG. 7 is started, first, in step 41, initial setting 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, and 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 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 in FIG. 7, 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 of the black dot (while keeping the thickness of the wall wide) is specified. The following (1) to (3) are compared with the width (length) of the region.

: When less than 5%, v = 0: Judge as non-wall part: When more than 95%, v = 1: Judge as wall: 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 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], and 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 or non-wall is undetermined is a content (v = 2) for which it is not known whether or not a wall exists. 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 dividing elements for the wall analysis is equal to k indicating the smallest No. of the array elements of the dividing 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 start addresses (ascending order) so that the divided area address data (array) is arranged in ascending order, and step 56 is executed.
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 degenerated (expressed in one range), and the process ends. 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 the 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 state, the sort state, 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, an example of a simple construction drawing wall recognition 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 rectangular area demarcated 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, actual walls are indicated in (a). That is, it is confirmed that the part is only the part which has been changed, and the state of the wall shown in (b) is recognized.

Further, by setting the nest variable = 2, the four regions in which the new wall candidate is recognized in FIG. 4B are divided by the newly recognized wall, respectively, 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, the investigation target area is subdivided and the wall is extracted until a new wall candidate is not extracted in any of the divided investigation target areas. 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 in accordance with 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. S1 to S18 in these figures are shown in FIG.
Correspond to the steps S1 to S18. In addition, an area division diagram, an actual wall state, and a processing progress ratio at each stage are also illustrated.

In the following description, steps are abbreviated as “S”. The image data of the construction drawing is input in S1 of FIG.
In step 2, automatic skew correction is performed. At S3, the whole drawing is examined, and at S3-1, the number of divided blocks D0 when the area of one whole drawing is first largely divided is set to 1, and the recognized number d0 of the large divided blocks is set to 0. I do. Therefore, at this time, since d0 / D0 = 0/1 = 0, the display of the processing progress ratio is set to “0%”.

At S4, the nest variable is set to 0. 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. Is extracted, and a wall other than the contour is found in two places.

Therefore, the result in S10 is YES, and in S1
The nest variable is set to 1 at 1, the area is subdivided (at each wall position) at S12, and YE is set at nest variable = 1 at S12-1.
S is determined to be S, and 3 is set to the number of divided blocks D0 when the area of one whole drawing is first divided into large areas in S12-2. At this time, since d0 / D0 = 0/3 = 0, the display of the processing progress ratio is also kept at "0%".

Thereafter, the flow returns to S5, and the investigation target area is limited to the leftmost area. Then, in S6 and S7, the wall was found in two places. In S8 and S9, the wall was not found.
If it is 0, the answer is YES, the nesting variable is set to 2 in S11, and the "nesting process" is repeatedly executed until the answer is NO in S14 of FIG. 19 and the nesting process ends. The wall extraction process is sequentially performed on three divided regions obtained by dividing the vertically long region on the left side by two newly discovered walls.

In the middle of FIG. 18, when the result of S10 becomes NO for the first time, that is, when all the wall candidates have been checked, the flow proceeds to S12-3. At this point, since the nest variable is 2, S12-5 is executed without adding a value to d0.
Proceed to. In S12-5, the current value of d0 / D0 is 0 /
Since 3 = 0, the display of the processing progress rate remains at “0%”.

In this example, no new wall is found, and the nest variable is decremented by 1 and returned to 1 in S16 of FIG. As a result, YES is obtained in S12-3, and S12-
Since d0 is incremented by 1 at d4, d0 = 1, so S12-
Since the value of d0 / D0 becomes 1/3 ≒ 33% in 5, the display of the processing progress ratio is set to “33%”.

Then, a wall is extracted in the same manner using the vertically long divided area in the middle as the investigation area, but no new wall candidate is found in this example. When the investigation of the divided area is completed, YES is determined in S12-3, and d is determined again in S12-4.
Since d0 = 2 because 0 is incremented by 1, d is calculated in S12-5.
Since the value of 0 / D0 is 2/3 ≒ 67%, the display of the processing progress ratio is set to “67%”.

Thereafter, in S15 and S5, the investigation target area is changed to a vertically long area on the right side, and one wall is found in S6 and S7. No new wall is found in S8-S9, but in S6-
Since one wall has been found in S7, the result is YES in S10, the nest variable is set to 2 in S11, the right vertical region is divided into two, and the nesting process is started. However, no new wall candidates are found in the upper and lower areas. During that time, the value of d0 / D0 is 2/3 ／
Since the processing progress ratio remains at 67%, the display of the processing progress ratio is also displayed as “67”.
% ".

When the nesting process is completed, the nest variable is decremented by 1 and returned to 1. At this time, YES is obtained in S12-3, and d0 is incremented by 1 in S12-4. Since the value of d0 / D0 becomes 3/3 = 100% in 5, the display of the processing progress ratio is set to “100%”.
Thereafter, “100%” is displayed as the processing progress ratio until the data of the wall candidate recognized in S17 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 number of nests indicates the depth of nesting (parent-child relationship) of child nest pointers. If no wall is recognized, the number of nests is zero. 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 the wall candidate based on the black dot count measurement array data, the region of the drawing is divided by the recognized wall candidate, and the black in the horizontal and vertical directions based on the image data in which the investigation target region 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 the 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.

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 CAD data between different models can be obtained.

FIG. 25 shows the processing progress rate measuring unit 13 shown in FIG.
5 is an example of a display screen of the progress of the recognition process during recognition, which is measured on the display unit 9 and displayed on the display unit 9. FIG. 26 is an example of a screen that displays the recognition processing progress state while displaying the drawing to be recognized.

For the image image data of the drawing to be recognized, all blocks divided based on the recognition result of the first wall candidate are displayed as outlines, and the blocks which have been subjected to the recognition processing are shaded or specified colors, for example. (For example, blue), and the block currently being recognized is blinked or displayed in another color (for example, yellow), so that the progress of the recognition process can be more easily understood. In the case where any trouble occurs during the recognition process, the display of the progress rate of the recognition process makes it easy to find the location where the problem occurs in the program.

Further, in this embodiment, a binary image extension process is performed to extend the outline of the portion composed of black dots by a predetermined number of dots in both the horizontal and vertical directions or in the diagonal direction, thereby connecting broken lines. If the recognition processing of a wall or the like is performed by converting the image data into a state in which the projected feature can be extracted, the following improvement in the recognition rate can be expected, so that accurate drawing restoration can be realized.

(1) Even if the wall is described by a single horizontal and vertical thin line, it can be surely recognized. (2) As a tool for drawing a line, even an architectural drawing described with a stationery such as a pencil in which a black line is coarse and discontinuous can be reliably recognized. (3) Even in drawings written freehand without using a ruler, etc., if the fluctuation of the line is offset in the description of the wall,
If the correctness in the horizontal and vertical directions is good, it can be recognized.

When it is difficult to correct the skew in the direction opposite to the house drawing in the handwritten drawing, the handwritten drawing may be described on a graph paper, and the skew correction may be performed using the grid of the graph paper. . If it is unavoidable to leave it to the user's manual work, a manual may be attached to draw attention to creating a facing drawing that requires as little skew correction as possible when reading the scanner.

[0128]

As described above, according to the construction drawing recognizing method and the recognizing apparatus of the present invention, a handwritten drawing in which a line which has been difficult to accurately recognize in the past is broken or a straight line is slightly inclined is described. It is easy to accurately recognize the outline and skeleton (especially walls) of construction drawings with poor description or image quality, such as drawings with relatively low contrast such as blueprints, drawings with lots of noise, and old construction drawings. it can. Furthermore, since the progress ratio (progress status) of the recognition process during the recognition process is specified during the recognition execution, such as a percentage (%), the elapsed time until the recognition execution operator can obtain the recognition result information is estimated. Is possible, and visibility is improved in planning work.

Further, based on the outline and skeleton initially recognized for the entire drawing, the target range is limited so as to divide the recognition target, and the horizontal direction of the image image data of the construction drawing is defined for each range. A new contour is created that creates the number measurement array data of black or white dots in the vertical direction and recognizes the outline and skeleton of the construction drawing within each of the limited areas based on the dot number measurement array data in both directions. Alternatively, if the process is repeated until the skeleton cannot be recognized, a skeleton such as a short wall can be surely recognized, and there is no possibility of erroneously recognizing a contour or a skeleton of a portion that does not actually exist.

If the recognized data is taken into a personal computer or the like and used, it is possible to easily and quickly create a construction drawing such as a sketch at the time of extension or remodeling. Furthermore, image data of a construction drawing can be input as image data obtained by run-length encoding by facsimile communication or the like, and the outline and skeleton of the construction drawing can be recognized.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a schematic configuration of an example 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;

FIG. 6 is a flowchart of a subroutine of a wall extraction (recognition) process in steps 7 and 9 in FIG. 5;

FIG. 7 is a flowchart for executing a bisection search method for recognizing the position of a wall.

FIG. 8 is an explanatory diagram showing a wall candidate in an investigation target area and an example of area division by the wall candidate.

FIG. 9 shows a nest N of an area group subdivided by a 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. 6;

FIG. 14 is an explanatory diagram of an area width dividing process in step 43 of FIG. 7;

FIG. 15 is an explanatory diagram of the judgment by the wall investigation of the target area in steps 44 and 45 in FIG. 7;

16 is an explanatory diagram showing an example of analyzing a wall sample (wall candidate) by the halving search process shown in FIG. 7;

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. 5;

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 showing an example of image data of the architectural drawing shown in FIG. 10 in which the investigation target area is limited, and the array data of the number of black dots measured 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 the arrangement data of the number of black dots 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 diagram of a display screen showing the progress ratio in the middle of the recognition process, which is measured by the process progress ratio measurement unit 13 in FIG. 1 and displayed on the display unit 9. FIG.

FIG. 26 is a diagram of a display screen showing the progress rate in the middle of the recognition process while displaying the drawing to be recognized.

[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: processing progress ratio measurement unit 14: bus

Claims (7)

[Claims] 1. A method for measuring the number of black or white dots in the horizontal and vertical directions of image image data obtained by reading an image of a construction drawing to create horizontal and vertical dot number measurement array data. A construction drawing recognition method characterized by recognizing a contour and a skeleton of a construction drawing based on dot number measurement array data and specifying a progress rate of the recognition process. 2. A method for measuring the number of black or white dots in the horizontal and vertical directions of image image data obtained by reading an image of a construction drawing to create horizontal and vertical dot number measurement array data. Recognize the outline and skeleton of the construction drawing based on the dot number measurement array data, subdivide the image image data to be recognized based on the recognized outline and skeleton, and The number of black or white dots in the horizontal direction and the vertical direction of the image image data is measured, and the dot number measurement array data in the horizontal and vertical directions is created again. Based on the created dot number measurement array data in both directions, the above-described limitation is performed. Recognizing the outline and skeleton of the construction drawing in each area until a new outline or skeleton cannot be recognized. A construction drawing recognition method characterized in that a current progress ratio up to the completion of recognition processing is specified by a ratio of the total number of regions when the recognition target region is first subdivided and the number of currently recognized regions. 3. An image data input means for inputting image image data obtained by reading an image of a construction drawing, and measuring the number of black or white dots in the horizontal direction and the vertical direction of the input image image data by the means. Dot number measurement array data creating means for creating vertical dot number measurement array data, and contour / skeleton recognition means for recognizing the outline and skeleton of a construction drawing based on the dot number measurement array data in both directions created by the means. And a means for measuring a progress rate of a series of recognition processes recognized by the means. 4. The construction drawing recognition apparatus according to claim 3, wherein said image data input means is an image reading means for reading an image of a construction drawing and inputting the image data. 5. An image data input means for inputting encoded image data obtained by encoding a run length of image image data obtained by reading an image of a construction drawing, and original image image data from the encoded image data inputted by the means. Dot number measurement array data creating means for creating horizontal and vertical dot number measurement array data by measuring the number of black or white dots in the horizontal and vertical directions, and a dot number measurement array in both directions created by the means An apparatus for recognizing construction drawings, comprising: contour / skeleton recognition means for recognizing the outline and skeleton of a construction drawing based on data; and means for measuring the progress rate of a series of recognition processes recognized by the means. 6. The construction drawing recognition apparatus according to claim 5, wherein said image data input means is image data receiving means for receiving and inputting said encoded image data by communication. 7. The construction drawing recognition apparatus according to claim 3, wherein the image data to be recognized is subdivided based on the outline and the skeleton recognized by the outline / skeleton recognition means. The number-of-dots-measuring-array data creating means is again created by the means for creating a number-of-dots-measuring-array-data for each range limited to each of the divided areas, and based on the created number-of-dots-measuring-array data in both directions. A means for repeating the contour / skeleton recognizing means to recognize the outline and the skeleton of the construction drawing within each of the limited ranges until a new contour or skeleton cannot be recognized; and measuring a progress rate of the recognition processing. Means for performing the current process up to the completion of the recognition process based on the ratio of the total number of regions when the recognition target region is first subdivided to the number of currently recognized regions. Construction drawings recognizer, characterized in that the means for measuring the rate.