An AI-powered system for detecting objects on technical drawings. Detection of rooms, walls, windows and doors, extraction of information from comlex PDF spreadsheets
Our client evaluates the construction of buildings and prepares bills of quantities. Buildings can be very different: a private house, an apartment building, or an office building.
To accurately prepare a bill of quantities and provide a price estimation, the following information needs to be extracted from a floor plan:
The documents usually contain multiple technical drawings, especially when it comes to multi-story buildings.
Ready-made solutions for optical character recognition (OCR) could not handle the task with enough accuracy or could not handle it at all due to the special characters used in the technical drawings, as well as the graphic nature of the drawings themselves.
The first step in analyzing any PDF file with a floor plan is to detect the location of the floor plan within the page. We have developed a segmentation machine learning model which automatically detects the drawing location. The system also gives users the ability to highlight the floor plan themselves.
The system detects technical drawing type and scale, and automatically generates a table of contents, making it easy to navigate large multi-page documents.
Another important objective we had to achieve is to detect various objects present in the floor plans, like doors, windows, different types of walls, etc., marked by special labels.
As OpenCV algorithms are not well suited for analyzing simple black and white geometric shapes, we have incorporated deep learning to increase the accuracy and reject false positives.
To start with, the user highlights the label that needs to be detected, one for every object group. After that, the processing starts, and the technical drawing is analyzed by the object recognition model. The results are as follows:
The user can manually go through the recognition results to fix any mistakes.
Floor plans come with a bill of quantities that contains information about the different labels used in the technical drawing, as well as information about the materials. PDF spreadsheets are not an ideal way to handle large amounts of data since they cannot be edited and the data cannot be sorted or filtered.
There is a number of readymade tools and solutions that can turn a PDF spreadsheet into an Excel one, though they work poorly with large complex spreadsheets that include merged cells and span across multiple PDF pages.
Readymade solutions do not handle merged cells well and often split them incorrectly Often when PDF spreadsheets span across multiple pages, their columns don’t line up which causes existing tools to process the data incorrectly If the text goes outside of its cell, readymade solutions split the text into multiple cells
We have developed a subsystem that scans the PDF spreadsheets and turns them into Excel spreadsheets without changing the original structure of the spreadsheet and keeping the data integrity.
A resulting system is a full-fledged tool for working with complex floor plans and accompanying tables. It reduces manual labor and greatly speeds up price estimation. The system is highly flexible and can be adjusted to analyze any PDF document and extract relevant information.