Ever had a need to manually delete text from a picture file? Image-to-text conversion technology made it possible. The procedure of turning images into text using optical character recognition (OCR) software. It is frequently used while trying to extract a document from an image. OCR Converter by cardscanner.co, scans papers in order to decipher the meaning contained in images. With the help of the text extraction technique, characters in a picture are transformed into editable text that can subsequently be saved as a Word or PDF document.
Machine learning document analysis is increasingly using unstructured data analytics as a key component (ML).
In this article, you will see how to extract text from photos using OCR converter.
Let’s Go!
Correct Alignment:
The degree to which the majority of scanned documents and text source pictures are out of alignment has a significant impact on the free online OCR result’s accuracy. According to how complex the input content is, a straightforward histogram projection to model-based alignment correction may be required. But you must try image to text technology to align your data properly.
The OCR Pipeline:
OCR Online is a method for removing text from scanned documents, photos, or videos and transforming it into a format that can then be modified, searched for, and used for NLP analytics tasks. To extract the text into an easily accessible, organized manner, it is important to combine computer vision (CV), recognition (ML), and text modules. Image converter help in this regard. The segmentation and preprocessing phases are the two primary parts of the CV modules. With the help of these two components, an OCR engine can be utilized to transform the raw text into output that is structured text during the post-processing phase.
Preprocessing:
The scanned input files frequently have improper dimensions, forms, or orientations. To improve the overall recognition accuracy of the OCR system, several preprocessing techniques must be used. After the free online OCR engine itself, this step is the most important. Cropping, alignment correction, distortion correction, binarization, and denoising are some of the preprocessing methods that are most frequently utilized (filtering out noise). At first, you must use an OCR converter to make it easy to manage your text.
Denoising and Deblurring:
The OCR engine can more accurately detect text if there is less noise and blur in the image of the source document. But first, you must change the form of the document from photo to text using an OCR converter. The impacts of Gaussian blur, salt-and-pepper sounds, and general noises can each be eliminated or reduced using image processing techniques like Gaussian, median, and bilateral filtering.
Using deep autoencoder denoising models, general denoising and deblurring can also be done.
Contrast and Sharpness:
The contrast between the text font and background can be enhanced to improve OCR accuracy. Similar to this, altering the sharpness of the text’s edges can aid in text segmentation both before and after OCR. Adaptive contrast enhancement based on histogram equalization is commonly used in OCR. Before it you must employ image to text converter so that it will be easy to change the contrast and sharpness of your document.
Crop:
As the first step in the process of getting ready for OCR, the relevant area of interest (ROI) that contains the pertinent text is chopped. It is feasible to automatically determine the approximate boundaries of all recognized text in the image by creating a cropping model, utilizing current OCR engines, specific heuristics, image processing, or both.
Conclusion:
Dedicated startup platforms can offer end-to-end tools for the complete life cycle of the unstructured document to structured text processing, even though open-source solutions can assist in the development of customized OCR pipelines and are useful for document pre-processing. Along with OCR-specific features, they offer tools for managing users and projects, extensive dashboards for tracking and visualizing statistics, labeling tools for documents with a semi-structured format, and more.
Large IT organizations could be able to offer modularized solutions for each step of the OCR pipeline. Businesses that employ OCR have a wide range of alternatives, including modular and end-to-end systems.