This leaves us with one single moving part in the equation to improve accuracy of OCR: The quality of the source image.Īs stated above, the better the quality of the original source image, the higher the accuracy of OCR will be.īut what means “image quality” in this case? Just think of it as “making it as easy as possible” for the OCR engine to distinguish a character from the background. Let’s assume you already settled on an OCR engine.
Java ocr tool how to#
How to Increase Accuracy With OCR Image Processing When it comes to proprietary OCR engines, it seems that ABBYY FineReader takes the pole position. The accuracy of Tesseract can be increased significantly with the right Tesseract image preprocessing toolchain. Furthermore, the Tesseract developer community sees a lot of activity these days and a new major version (Tesseract 4.0) is on its way. The Tesseract OCR accuracy is fairly high out of the box and can be increased significantly with a well designed Tesseract image preprocessing pipeline. While many OCR engines are using the same type of algorithms, each of them comes with its own strengths and weaknesses.OCR accuracy comparison is difficult and choosing the right OCR engine mostly depends on your specific use-case, the allocated budget and how it integrates into an existing system.Īt the moment of writing it seems that Tesseract is considered the best open source OCR engine.
Java ocr tool free#
There are various OCR engines available, ranging from free open source OCR engines to proprietary solutions with a hefty price tag.
Java ocr tool software#
The OCR EngineĪn OCR engine is the software which actually tries to recognize text in whatever image is provided. The better the quality of original source image, the easier it is to distinguish characters from the rest, the higher the accuracy of OCR will be. But if the original source itself is not clear, then OCR results will most likely include errors. if the human eyes can see the original source clearly, it will be possible to achieve good OCR results. If the quality of the original source image is good, i.e. When it comes to improving OCR accuracy, you basically have two moving parts in the equation. The more accurate characters are recognized, the less “fixing” on a word level is required. In this article we will focus on improving the accuracy on character level. Words containing uncertain characters can then be “fixed” by finding the word inside the dictionary with the highest similarity. all words of in the English language corpus).
English), the recognized words can be compared to a dictionary of all existing words (e.g. If the language of the text is known (e.g. To improve word level accuracy, most OCR engines make use of additional knowledge regarding the language used in a text. You can then either count how many characters were detected correctly (character level accuracy), or count how many words were recognized correctly (word level accuracy). Measuring OCR accuracy is done by taking the output of an OCR run for an image and comparing it to the original version of the same text. While an accuracy of 99.9% means that 1 out of 1000 characters is uncertain.
An accuracy of 99% means that 1 out of 100 characters is uncertain. How accurate an OCR software is on a character level depends on how often a character is recognized correctly versus how often a character is recognized incorrectly. In most cases, the accuracy in OCR technology is judged upon character level. When it comes to OCR accuracy, there are two ways of measuring how reliable OCR is: In this article, we cover different techniques to improve OCR accuracy and share our takeaways from building a world-class OCR system for Docparser. If you are in the midst of setting up an OCR solution and want to know how to increase the accuracy levels of your OCR engine, keep on reading… Getting to OCR accuracy levels of 99% or higher is however still rather the exception and definitely not trivial to achieve.Īt Docparser we learned how to improve OCR accuracy the hard way and spent weeks on fine-tuning our OCR engine. Optical Character Recognition ( OCR) technology got better and better over the past decades thanks to more elaborated algorithms, more CPU power and advanced machine learning methods.
0 kommentar(er)
