So, for detection it will give me cropped images which will contain text part only.
Here, I have trained yolov4 model on different text. Step3: Mapping room names with it’s corresponding dimensions.īelow is the code for detection. Step2: Used easy OCR to detect the text from the cropped images. Step1: Used object detection model (yolov4) and crop the detected images which contains only textual part from the floor plan. You can try on some images using this link.įrom this image, I want to extract a list of all room names with it’s proper dimensions. It uses machine learning to understand your images with industry-leading prediction accuracy. Google Cloud vision API: It is the most powerful API available for the text extraction.Online tools available for text extraction: You can go through this blog to read more real time use cases of OCR. Text extraction from the image can be used in number of applications such as reading information from scanned documents, social media data analysis, in healthcare sector, data scientists analyze the text using advanced data science techniques, bank uses the text extraction to compare the bank statements and there are many more applications of the text extraction. You can go through this blog to read more about OCR technology.
DIFFERENT TEXT EXTRACTOR MANUAL
It also eliminates the need for manual data entry.
OCR software singles out letters on the image, puts them into words and then puts the words into sentences, thus enabling access to and editing of the original content. An OCR program extracts and repurposes data from scanned documents, camera images and image-only pdfs. Optical Character Recognition is referred as text recognition.
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