Tesseract ocr download install#
Tesseract ocr download full#

Return cv2.erode(image, kernel, iterations = 1) Img = cv2.imread('excel-invoice-template.png', 0) Return cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel) Kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 5))ĭilate = cv2.dilate(thresh, kernel, iterations=5) Thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

Tesseract ocr download full#

#dilate to merge text to meaning full text

Tesseract ocr download install#

To run tesseract OCR we should install it in our system, Download executable Tesseract from this link Now let’s get dirt on our hands with python code using open CV and tesseract OCR The Tesseract after processing gives a printed text of the image taken as the output. The trained data set is fed to the Tesseract engine. Leptonica is an open-source library that contains software used for image processing and analysis. Tesseract acts as the brain of the application. This input image is pre-processed and then pre-processed image is fed to the Tesseract. In the following flowchart of the OCR system, the image is taken by the camera acts as the input image which has text in it. With just a single line of code, it will extract text from the image. Tesseract can extract text from a wide variety of languages. Tesseract is an API, used directly to extract text (printed) from images. Tesseract is an open-source OCR engine used to extract text, It is available under the Apache 2.0 license. To learn image processing techniques follow click here. If you don’t have any idea about OpenCV, no worries, Please follow our Image Processing section for an easy understanding of OpenCV.īut here we will go through few image processing techniques that will be necessary for OCR steps. These preprocessing steps can have many sub-processes depends on the use cases. The basic process we follow in OCR are preprocessing, text detection, text extraction, and post-processing, OCR extracts the text from any image that contains text to machine-encoded text, whether the image can be a scanned document, a photo of a document, a photo from a scene (say vehicle number, landscape names, street address ). Now I am going to show the text extraction using Python, OpenCV, and tesseract OCRĪs mentioned earlier, OCR stands for optical character recognition. As we have gone through the basic introduction and brief history of OCR in the previous blog.