CN116206325A - System and method for identifying house type graph - Google Patents
System and method for identifying house type graph Download PDFInfo
- Publication number
- CN116206325A CN116206325A CN202310110247.8A CN202310110247A CN116206325A CN 116206325 A CN116206325 A CN 116206325A CN 202310110247 A CN202310110247 A CN 202310110247A CN 116206325 A CN116206325 A CN 116206325A
- Authority
- CN
- China
- Prior art keywords
- house type
- unit
- house
- image
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/40—Document-oriented image-based pattern recognition
- G06V30/42—Document-oriented image-based pattern recognition based on the type of document
- G06V30/422—Technical drawings; Geographical maps
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/51—Indexing; Data structures therefor; Storage structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/53—Querying
- G06F16/532—Query formulation, e.g. graphical querying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/18—Extraction of features or characteristics of the image
- G06V30/186—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/19—Recognition using electronic means
- G06V30/191—Design or setup of recognition systems or techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06V30/19173—Classification techniques
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses an identification system of a house type diagram, which comprises a function selection module, a function selection module and a control module, wherein the function selection module is used for selecting system functions to be performed; the data storage module is used for storing the house type graph data; the calling module is used for calling the house type graph data in the data storage module; the input module is used for scanning and inputting the house type graph image; the image processing module is used for carrying out definition processing on the received house type image; the text recognition module is used for recognizing and extracting the characters marked on the house type graph, and automatically classifying and calculating the number of functional areas and the house area of the current house type graph; and the label module is used for labeling the types of the house type graphs. The invention can store the new house type graph into the database, has quick storage process, can automatically identify house information, reduces the complexity of manually adding information, improves the working efficiency, and can quickly screen the house type graph meeting the requirements from the database during later calling so as to provide reference value for customers.
Description
Technical Field
The invention relates to the technical field of house type drawings, in particular to a system and a method for recognizing a house type drawing.
Background
With the rapid development of the real estate industry, various house types appear in the market, and the demands of people on decoration design are also increasing. At present, people need to find a decoration company first to design a home decoration style for self-home body measurement, and in the design process, if the people want to obtain a satisfactory decoration design house type diagram, the people often need to fully communicate with a user, and the people can obtain a desired home decoration scheme by modifying the design scheme for many times, so that the process is time-consuming and labor-consuming. Therefore, in the early stage, a designer generally chooses to provide some house pattern templates for the customer, determines the approximate direction and decoration style with the customer, and enables the customer to provide approximate thinking according to the modules, so that a great amount of time can be saved in the later stage of design, and the opinion of the customer can be rapidly grasped.
Most designers are in earlier stage work, the material library of themselves is insufficient, the house type pattern templates which can be provided for customers are limited, so that in order to realize design sharing and resource sharing, staff inside are convenient to check and reference the house type pattern templates which are finished, therefore, drawn house type patterns can be stored into a system, but various information data are required to be manually added when the house type patterns are stored each time, and the operation is complex and time is wasted.
Disclosure of Invention
The invention aims to provide a system and a method for identifying a house type graph, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the system for identifying the house type graph comprises a function selection module, a function selection module and a control module, wherein the function selection module is used for selecting system functions to be performed; the data storage module is used for storing house type graph data; the calling module is used for calling the house type graph data in the data storage module; the input module is used for scanning and inputting the house type graph image; the image processing module is used for carrying out definition processing on the received house type image; the text recognition module is used for recognizing and extracting the characters marked on the house type graph and automatically classifying and calculating the number of functional areas and the house area of the current house type graph; and the label module is used for labeling the types of the house type graphs.
Further, the function selection module comprises a new building unit and a screening unit, wherein the new building unit is used for storing the new house type graph into the database, and the screening unit is used for screening the house type graph meeting the requirements in the database.
Further, the calling module comprises a keyword input unit and an output unit, wherein the keyword input unit can input types of the household pattern to be searched, and the output unit can arrange the searched household pattern in a descending order according to the matching degree.
Further, the input unit comprises a camera acquisition unit, a picture input unit and a drawing input unit, wherein the camera acquisition unit is used for directly scanning the house type picture of the paper edition, the picture input unit is used for inputting the house type picture of the electronic edition, and the drawing input unit is used for inputting the house type picture drawn by drawing software.
Further, the image processing unit comprises a gray level processing unit, a binarization processing unit and an edge detection processing unit, wherein the gray level processing unit is used for converting a colored house type image into a gray level image, the binarization processing unit is used for processing the gray level image into a black-and-white image, and the edge detection processing unit is used for enhancing contour edges, details and gray level jump parts in the image to form a complete object boundary.
Further, the text recognition module comprises a first recognition unit, a second recognition unit and a calculation unit, wherein the first recognition unit is used for recognizing numbers representing the house area on the house type graph, the second recognition unit is used for recognizing characters on the house type graph, and the calculation unit automatically calculates the total area of the house.
Further, the label module comprises a household pattern type unit, a style unit and a keyword input unit, wherein the household pattern type unit is used for inputting the type of the stored household pattern, and the style unit is used for inputting the style of the stored household pattern.
Further, the data storage module comprises a local storage unit and a cloud storage unit, wherein the local storage unit is used for storing the information data of the house type graph in the local storage card, and the cloud storage unit is used for storing the information data of the house type graph on the cloud end.
Further, a method for using the system for identifying the house type graph comprises the following steps:
s1, entering a system, and selecting the functions: firstly, storing a new house type diagram, and secondly, screening similar house type diagrams;
s2, if the function of screening similar house types is selected, the following steps are carried out: popup dialog, input screening conditions (such as several rooms, halls, house areas, etc.) in dialog, search in database according to the input conditions, and arrange in descending order of similarity;
s3, if the function of storing the new house type graph is selected, the following steps are carried out: scanning house type graph images; processing the definition of the image; identifying the letters and numbers on the image; a dialog box is popped up to display the distribution structure (N rooms M and X guard) of the current house type diagram and the house type area, and the error data can be modified; labeling the current house type graph; the data is saved to a database.
Further, in the step 3, the processing of the sharpness of the image adopts the following steps:
s31: processing the acquired image to obtain a house type gray level diagram:
s32: binarization processing is carried out on the house type gray level graph so as to remove image background:
s33: processing the image with the background removed to obtain an edge image, and then cutting the edge image to obtain a house type layout outline drawing;
s34: and processing the house type layout outline map by adopting a wall threshold segmentation method, and identifying a room.
Compared with the prior art, the invention has the following beneficial effects:
the invention can store the new house type graph into the database, has quick storage process, can automatically identify house information, reduces the complexity of manually adding information, improves the working efficiency, and can quickly screen the house type graph meeting the requirements from the database during later calling so as to provide reference value for customers.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic block diagram of the present invention;
fig. 2 is a schematic flow chart of the present invention.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: a system for recognizing house type graph includes
The function selection module is used for selecting system functions to be performed;
the data storage module is used for storing house type graph data;
the calling module is used for calling the house type graph data in the data storage module;
the input module is used for scanning and inputting the house type graph image;
the image processing module is used for carrying out definition processing on the received house type image;
the text recognition module is used for recognizing and extracting the characters marked on the house type graph and automatically classifying and calculating the number of functional areas and the house area of the current house type graph;
and the label module is used for labeling the types of the house type graphs.
The function selection module comprises a new building unit and a screening unit, wherein the new building unit is used for storing the new house type graph into the database, and the screening unit is used for screening the house type graph meeting the requirements in the database.
The calling module comprises a keyword input unit and an output unit, wherein the keyword input unit can input types of the household patterns to be searched, and the output unit can arrange the searched household patterns in a descending order according to the matching degree.
The input unit comprises a camera acquisition unit, a picture input unit and a drawing input unit, wherein the camera acquisition unit is used for directly scanning the house type picture of the paper edition, the picture input unit is used for inputting the house type picture of the electronic edition, and the drawing input unit is used for inputting the house type picture drawn by drawing software.
The image processing unit comprises a gray level processing unit, a binarization processing unit and an edge detection processing unit, wherein the gray level processing unit is used for converting a colored house type image into a gray level image, the binarization processing unit is used for processing the gray level image into a black-and-white image, and the edge detection processing unit is used for enhancing contour edges, details and gray level jump parts in the image to form a complete object boundary.
The text recognition module comprises a first recognition unit, a second recognition unit and a calculation unit, wherein the first recognition unit is used for recognizing numbers representing the house area on the house type graph, the second recognition unit is used for recognizing characters on the house type graph, and the calculation unit automatically calculates the total area of the house.
Specifically, the first recognition unit recognizes the number representing the area on the house type graph, i.e. the number is followed by m 2 Is a number of (2) to extract Nm 2 、N+1m 2 、M+2m 2 、、、、、、N+Mm 2 Data are automatically summed to obtain a house area S, and a calculation formula is as follows:
S area of =Nm 2+ N+1m 2 +M+2m 2 +、、、、、、+N+Mm 2 ；
The second recognition unit extracts the names of the functional areas on the house type map, such as a main sleeping room, a secondary sleeping room, a toilet, a living room, a kitchen and the like, and automatically pops up the first dialog box to display the kitchen of the room A, the room B, the room C, the room D (wherein A is equal to the number of the main sleeping room plus the number of the side sleeping rooms), after the information is correct, clicks the next step, if other functional areas are arranged on the house type map, pops up the new second dialog box again to display the names of the other functional areas, such as a house building room, a clothes room, a dressing room and the like, and if no other functional areas exist, the storage is directly ensured, and as most of the house type map is the kitchen of the room A, the room B, the room C, the room D and the room C, the kitchen are more than the characters of the places recognized by the second recognition unit can pop up after the room A, the time is saved, and the efficiency of recognizing and storing the house type map is improved.
The label module comprises a household pattern type unit, a style unit and a keyword input unit, wherein the household pattern type unit is used for inputting the type of the stored household pattern, and the style unit is used for inputting the style of the stored household pattern.
The data storage module comprises a local storage unit and a cloud storage unit, wherein the local storage unit is used for storing information data of the house type graph in a local storage card, and the cloud storage unit is used for storing the information data of the house type graph on a cloud end.
The application method of the household pattern recognition system comprises the following steps:
s1, entering a system, and selecting the functions: firstly, storing a new house type diagram, and secondly, screening similar house type diagrams;
s2, if the function of screening similar house types is selected, the following steps are carried out: popup dialog, input screening conditions (such as several rooms, halls, house areas, etc.) in dialog, search in database according to the input conditions, and arrange in descending order of similarity;
s3, if the function of storing the new house type graph is selected, the following steps are carried out: scanning house type graph images; processing the definition of the image; identifying the letters and numbers on the image; a dialog box is popped up to display the distribution structure (N rooms M and X guard) of the current house type diagram and the house type area, and the error data can be modified; labeling the current house type graph; the data is saved to a database.
In the step 3, the processing of the definition of the image adopts the following steps:
s31: processing the acquired image by using opencv technology to obtain a house type gray scale map:
s32: OTSU binarization processing is carried out on the house type gray level diagram so as to remove image background:
s33: obtaining an edge image of the image with the background removed by using a Sobel operator technology, and then cutting the edge image to obtain a house type layout outline drawing;
s34: and processing the house type layout outline map by adopting a wall threshold segmentation method, and identifying a room.
The specific implementation mode is as follows: when the system is used, user information is registered first, the system is logged in, the system comprises two functional modules, namely a house type map storage function and a similar house type map screening function, if the similar house type map screening function is selected, a dialog box is popped up, screening conditions such as a few rooms, a few halls, a house area, a house type map style and the like can be input into the dialog box, searching can be performed in a database after the screening conditions are determined, and the screening conditions are arranged in a descending order according to the similarity; if the function of storing the house type map is selected, the house type map image needs to be scanned, a camera can be used for directly shooting the house type map, or the house type map image (with the format of PNG, GIF, JPEG, TIFF, BMP and the like) is imported, or the house type map image (with the format of CAD) is imported, then the definition of the house type map image is processed, the characters and the numbers on the image are identified, the data are extracted, a dialog box is popped up, the distribution structure of the current house type map can be automatically displayed, the distribution structure is N-room M-room X-shaped and house type area (N, M, X are natural numbers), the wrong data can be modified, a new dialog box can be popped up again after clicking, the corresponding label of the house type map can be selected in the dialog box, such as the house type map type (the house type map is divided into a standard type map and a nonstandard house type map), the style type (such as simple wind, european wind and the like), the current house type map is labeled, the current house type map can be conveniently and rapidly searched in the later stage, and the current house type map data can be saved in a database by clicking a save button at this moment, and the later stage is convenient to call.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The utility model provides a recognition system of house type diagram which characterized in that: comprising
The function selection module is used for selecting system functions to be performed;
the data storage module is used for storing house type graph data;
the calling module is used for calling the house type graph data in the data storage module;
the input module is used for scanning and inputting the house type graph image;
the image processing module is used for carrying out definition processing on the received house type image;
the text recognition module is used for recognizing and extracting the characters marked on the house type graph and automatically classifying and calculating the number of functional areas and the house area of the current house type graph;
the label module is used for labeling the types of the house type graphs;
the function selection module comprises a new building unit and a screening unit, wherein the new building unit is used for storing the new house type graph into the database, and the screening unit is used for screening the house type graph meeting the requirements in the database;
the calling module comprises a keyword input unit and an output unit, wherein the keyword input unit can input types of the household patterns to be searched, and the output unit can arrange the searched household patterns in a descending order according to the matching degree.
2. A system for identifying a house pattern according to claim 1, characterized in that: the input unit comprises a camera acquisition unit, a picture input unit and a drawing input unit, wherein the camera acquisition unit is used for directly scanning the house type picture of the paper edition, the picture input unit is used for inputting the house type picture of the electronic edition, and the drawing input unit is used for inputting the house type picture drawn by drawing software.
3. A system for identifying a house pattern according to claim 1, characterized in that: the image processing unit comprises a gray level processing unit, a binarization processing unit and an edge detection processing unit, wherein the gray level processing unit is used for converting a colored house type image into a gray level image, the binarization processing unit is used for processing the gray level image into a black-and-white image, and the edge detection processing unit is used for enhancing contour edges, details and gray level jump parts in the image to form a complete object boundary.
4. A system for identifying a house pattern according to claim 1, characterized in that: the text recognition module comprises a first recognition unit, a second recognition unit and a calculation unit, wherein the first recognition unit is used for recognizing numbers representing the house area on the house type graph, the second recognition unit is used for recognizing characters on the house type graph, and the calculation unit automatically calculates the total area of the house.
5. A system for identifying a house pattern according to claim 1, characterized in that: the label module comprises a household pattern type unit, a style unit and a keyword input unit, wherein the household pattern type unit is used for inputting the type of the stored household pattern, and the style unit is used for inputting the style of the stored household pattern.
6. A system for identifying a house pattern according to claim 1, characterized in that: the data storage module comprises a local storage unit and a cloud storage unit, wherein the local storage unit is used for storing information data of the house type graph in a local storage card, and the cloud storage unit is used for storing the information data of the house type graph on a cloud end.
7. The method for using a system for identifying a house type graph according to any one of claims 1-6, characterized in that: the method comprises the following steps:
s1, entering a system, and selecting the functions: firstly, storing a new house type diagram, and secondly, screening similar house type diagrams;
s2, if the function of screening similar house types is selected, the following steps are carried out: popup dialog box, input screening condition in dialog box, search in database according to input condition, and arrange according to similarity descending order;
s3, if the function of storing the new house type graph is selected, the following steps are carried out: scanning house type graph images; processing the definition of the image; identifying the letters and numbers on the image; a dialog box is popped up to display the distribution structure of the current house type diagram and the house type area, and error data can be modified; labeling the current house type graph; the data is saved to a database.
8. The method for using the system for recognizing the house type drawing according to claim 7, wherein: in the step 3, the processing of the definition of the image adopts the following steps:
s31: processing the acquired image to obtain a house type gray level diagram:
s32: binarization processing is carried out on the house type gray level graph so as to remove image background:
s33: processing the image with the background removed to obtain an edge image, and then cutting the edge image to obtain a house type layout outline drawing;
s34: and processing the house type layout outline map by adopting a wall threshold segmentation method, and identifying a room.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310110247.8A CN116206325A (en) | 2023-02-14 | 2023-02-14 | System and method for identifying house type graph |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310110247.8A CN116206325A (en) | 2023-02-14 | 2023-02-14 | System and method for identifying house type graph |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116206325A true CN116206325A (en) | 2023-06-02 |
Family
ID=86516748
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310110247.8A Pending CN116206325A (en) | 2023-02-14 | 2023-02-14 | System and method for identifying house type graph |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116206325A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844614A (en) * | 2017-01-18 | 2017-06-13 | 天津中科智能识别产业技术研究院有限公司 | A kind of floor plan functional area system for rapidly identifying |
| CN109840376A (en) * | 2019-01-28 | 2019-06-04 | 司空科技股份有限公司 | The read method of floor plan, device and storage medium |
| CN110765540A (en) * | 2019-11-04 | 2020-02-07 | 深圳镜界智能科技有限公司 | System and method for matching house type graph with various self-contained designs |
| CN110781851A (en) * | 2019-10-31 | 2020-02-11 | 武汉攻壳科技有限公司 | Method for identifying decoration house type graph based on picture |
| CN112232131A (en) * | 2020-09-18 | 2021-01-15 | 云南省设计院集团有限公司 | Method and system for automatically extracting house type characteristic indexes based on computer vision |
-
2023
- 2023-02-14 CN CN202310110247.8A patent/CN116206325A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844614A (en) * | 2017-01-18 | 2017-06-13 | 天津中科智能识别产业技术研究院有限公司 | A kind of floor plan functional area system for rapidly identifying |
| CN109840376A (en) * | 2019-01-28 | 2019-06-04 | 司空科技股份有限公司 | The read method of floor plan, device and storage medium |
| CN110781851A (en) * | 2019-10-31 | 2020-02-11 | 武汉攻壳科技有限公司 | Method for identifying decoration house type graph based on picture |
| CN110765540A (en) * | 2019-11-04 | 2020-02-07 | 深圳镜界智能科技有限公司 | System and method for matching house type graph with various self-contained designs |
| CN112232131A (en) * | 2020-09-18 | 2021-01-15 | 云南省设计院集团有限公司 | Method and system for automatically extracting house type characteristic indexes based on computer vision |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112101357B (en) | RPA robot intelligent element positioning and picking method and system | |
| US20210287091A1 (en) | Neural network training method and image matching method and apparatus | |
| CN111275082A (en) | Indoor object target detection method based on improved end-to-end neural network | |
| CN114641753A (en) | Composite data generation and Building Information Model (BIM) element extraction from floor plan drawings using machine learning | |
| CN111862119A (en) | Semantic information extraction method based on Mask-RCNN | |
| CN114092700B (en) | Ancient character recognition method based on target detection and knowledge graph | |
| CN110765540A (en) | System and method for matching house type graph with various self-contained designs | |
| CN117058271A (en) | Method and computing device for generating commodity main graph background | |
| CN110910401A (en) | Semi-automatic image segmentation data annotation method, electronic device and storage medium | |
| JP2016103759A (en) | Image processing apparatus, image processing method, and program | |
| CN115082776A (en) | Electric energy meter automatic detection system and method based on image recognition | |
| CN111860487A (en) | Inscription marking detection and recognition system based on deep neural network | |
| CN112613367A (en) | Bill information text box acquisition method, system, equipment and storage medium | |
| CN115546824B (en) | Taboo picture identification method, apparatus and storage medium | |
| CN116206325A (en) | System and method for identifying house type graph | |
| CN111079742A (en) | Method for accurately positioning text block of text area image in scanning test paper | |
| JPH09167233A (en) | Image processing method and device therefor | |
| CN111612890B (en) | Method and device for automatically generating three-dimensional model by two-dimensional house type graph and electronic equipment | |
| JPH07168910A (en) | Document layout analysis device and document format identification device | |
| CN113570427A (en) | System for extracting and identifying on-line or system commodity characteristic information | |
| JP2011118481A (en) | Program and apparatus for processing image | |
| Bhatt et al. | Text Extraction & Recognition from Visiting Cards | |
| CN114764776A (en) | Image labeling method and device and electronic equipment | |
| Cheng et al. | Research on recognition method of interface elements based on machine learning | |
| CN114202761B (en) | Information batch extraction method based on picture information clustering |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |