8/22/2023 0 Comments Open source ocr tool![]() ![]() The tool must first detect where the text is positioned in the image and then recognize that text, or convert it to a plain string. Though often simply referred to as OCR, OCR contains two parts – detection and recognition. Recognition is only part of the solution. Read more about typical image pre-processing techniques used prior to OCR by visiting Survey on Image Preprocessing Techniques to Improve OCR Accuracy. Scanned documents can also contain alignment issues that should be fixed by de-skewing and perspective or curvature correction. These techniques can include binarization, noise removal, and increasing contrast and sharpness to improve visibility of the text. Some image pre-processing techniques can be applied to improve the visibility of textual information in images or scanned data. ![]() On the other hand, poor quality images can swiftly degrade the performance of OCR tools. If the image quality is crisp and clear to the human eye, an OCR tool is more likely to convert the image to a text string with high accuracy. OCR performance, regardless of the algorithm or technology behind it, heavily depends on image quality. To read up on one of our projects on document intelligence, see our recent Data Science Network article Document Intelligence: The art of PDF information extraction. In past projects, we worked with scanned images of natural health products, scanned forms and scanned financial statements that require text detection and recognition among other things for the purpose of converting unstructured data into usable structured data. OCR tools are required in IE when dealing with images or scanned documents. IE tools can reduce manual effort, save time and reduce the risk of human error. Usually, information from unstructured data is obtained by manually looking through the unstructured data which is time-consuming, and error-prone. The extracted information is used to prepare data for analysis. Information extraction (IE) is the process of extracting useful structured information from unstructured data in the form of text files, images, videos, audio clips, and other types. ![]() To see more H-mean scores, visit " Character Region Awareness for Text Detection". CRAFT is a state-of-the-art OCR tool for scene text detection (images with regions of text at various angles in a complex background) with the highest Harmonic Mean (H-mean) score across multiple public datasets compared to other open-source scene text detection OCR tools. Due to regular meaningful upgrades to the source code, Tesseract is very popular in the open-source community. Since then, Google has continued developing it and releases improved versions every few years for use at no cost. Tesseract was developed by Hewlett-Packard (HP) in the 1980s as a proprietary software and later made to be open source in 2005. This article compares two popular open-source OCR tools used to recognize printed text used in information extraction projects at Statistics Canada's Data Science Division: Google's OCR Engine Tesseract and Clova AI's OCR tool CRAFT. There are many open-source OCR tools in various programming languages that can be used without any data or machine learning training prior to their use. Text can be any combination of printed or handwritten characters and includes symbols, numbers, punctuation marks, etc. Optical Character Recognition (OCR) is used to capture text from photos containing some textual information, as well as from scanned documents, such as forms, invoices or reports.
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