LSTMs are highly efficient at learning from a long sequence of words and predicting the next word. The OCR engine has its origins in OCRopus’ Python-based LSTM (Long Short Term Memory) which is a class of Recurrent Neural Network (RNN). Text recognition is then possible with the adaptive classifier that needs to be trained for producing effective results as shown below. The third is the main step as it extracts the character outline and does OCR to further organize the text into lines and regions. The first step begins with color sensing followed by converting the image into binary images. The OCR engine supports various output formats including plain text, HTML, PDF, TSV, and XML.
Tesseract is an example based system working on a set of rules that can be easily modified depending on the requirement. With Tesseract, providers of artificial intelligence development services are able to achieve optimum accuracy and efficiency with the following structural advantages- a) Flexibility in Training Tesseract’s compatibility with several programming languages makes it an efficient tool for extracting text from large volumes of documents and images.
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The latest version, Tesseract 4.0, is available under the Apache 2.0 license and can detect over 100 languages from images and videos. Tesseract is an open-source Optical Character Recognition (OCR) engine originally initiated as a research paper by Hewlett Packard and later developed by Google.
Let’s explore how Tesseract OCR enhances traditional optical character recognition services for building enterprise-grade AI solutions. This article serves as a comprehensive guide to install, run, and implement Tesseract OCR with Python and OpenCV. Powered by deep learning, Tesseract OCR is one such AI engine that enables computers to capture and extract text from scanned documents. Training machines to understand and record human languages is another significant step toward making artificial intelligence (AI) more human.