A lot of data is today hidden behind an image. PDFs and scanned images are some of the many forms of images from which important text and data has to be extracted. The biggest challenge to extract text from JPEG file or any image file is the number of errors it has with the manual processes. Apart from that, the manual process is highly time-consuming as well.
With the advancement in technologies, IT engineers and software developers have come up with automation solutions that can extract text from JPEG file and other similar formats. This has made the entire process of data capture and extraction, seamless, and effective and has also reduced the operational cost for enterprises.
Datasets that have been extracted from images and other related formats have helped enterprises to design seamless workflows, create reports and take decisions swiftly. We will know to understand how the process of text extraction from a JPEG file works.
Process to Extract Text from JPEG File
Image Source File: The JPEG file from which the text has to be extracted.
Detection of Text: Identifying the area which has the text on the image.
Data Extraction: Extracting text from the region where it is present.
Detection of Text Process
Localization:
The maximum amount of background, that is surrounded by the text, is removed in the step of localization. This process takes place by either analyzing the component or the region based methods. Region based methods can be further calssified into two categories, Region Growing Method and Region Splitting & Merging Method.
Verification:
Verification, also known as the classification stage, can be either supervised or unsupervised. Supervised algrotihms is used at this stage as it is aware about the attributes like color, texture, size and so on. Unsupervised algorithms simply do not have the knowledge about it.
Data Extraction Process
Segmentation:
Segmentation process is done to extract the bounded text from the background of the JPEG image. Binarization and character segmentation are some algorithms that are used in this process. Binarization segmentations uses the means cluster algotihm to transform the color images to grayscale. It has the ability to enhance the capability of text recognition. Character segmentation on the other hand is applied directly to grayscale images. This enables for text recognition for a single string and strings that are broken or joined together to provide effective results.
Recognition:
The final step in the process is recognition of the text that converts string characters to character of words or multiple strings. It is done using following two techniques: character recognition and word recognition. The sole motive for this process is to create a visual representation of words for human users to see it.
Examples of Techniques used to Extract Text from JPEG File
Below mentioned are some of the many common technologies that have been applied to extract text from JPEG file. Some of them have been in use before the arrival of deep learning technology.
Optical Character Recognition
Optical Character Recognition (OCR) is a widely used technology to extract text from image files and documents. KlearStack AI also uses OCR, along with machine learning and artificial intelligence to extract data accurately.
OCR has been mainly used for data entry purposes. Data extraction takes place from documents such as invoices, passports, receipts, PDFs and so on using OCR.
Stroke Width Transformation
Stroke Width Transformation (SWT) is a technique of text exraction that is used mainly for nautral images and not for scanned documents, mails or prints. A local image operator will check the width of the stroke in this method. This width will then detect using pixel on pixel that corresponds with each other. Using this technique, it will be able to figure out what portion of the image describes a particular string.
Maximum Stable Extremal Region
Maximum Stable Extremal Region (MSER) technology takes the help of blob detection method. Blob detection regions in an image exhibit various properties like color and brightness when compared to its surrounding background. This technique also takes help of a robust wide-line algorithm. The purpose of this algorithm is to create corresponding points within the image for the purpose of text detection.
Conclusion
Automation solutions coupled with artificial intelligence and machine learning technologies have made it extremely easy for enterprises to extract text from JPEG and other image format files. Plenty of solutions are available that not only help in accurate data extraction from image files but also enable end-to-end document processing.
KlearStack AI is a leading intelligent document processing platform that has the ability to accurately extract text and data from structured and unstructured documents. It also has the ability to extract data with highest level of accuracy from unseen documents. Schedule a demo or get in touch with our experts to know more about KlearStack AI.
