A Python-based tool that leverages the power of OpenCV and Tesseract OCR to cleanse images and extract text from them in a bulk manner.
Before proceeding, ensure that the Tesseract OCR engine is installed on your system. Tesseract OCR is an open-source Optical Character Recognition engine used to recognize textual data from images.
The program runs in two significant steps:
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Image Cleaning: Through OpenCV, the program processes each image, reducing noise and enhancing the image quality to ensure optimal text extraction.
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Text Extraction: Utilizing the Tesseract OCR engine, the program extracts textual data from the cleaned images, writing the result to individual text files.
Here's a breakdown of the core functions and how they interact:
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clean_image(): This function accepts an image as input, applying several image processing techniques via OpenCV to clean the image and eliminate noise. -
extract_text(): This function takes two parameters: the path to an image file and the path to an output text file. It loads the image, cleans it using theclean_image()function, and then uses Tesseract OCR to extract text from the cleaned image. The extracted text is then saved to the specified output text file. -
main(): This function serves as the orchestrator. It retrieves a list of image files in a specified directory, processing each image file using theextract_text()function. The resulting output text files are saved in a separate directory, with names following the formattext1.txt,text2.txt, and so on.
Please note that the quality of the image impacts the accuracy of the text extraction. Better image quality would invariably lead to more accurate text extraction. Post-processing such as spell-checking might also be necessary to handle OCR's occasional recognition errors.