Comparative Analysis of Text Extraction from Color Images using Tesseract and OpenCV

Image-based Text Extraction has a growing requirement in today's generation. Students, doctors, and engineers generate a lot of images every day. It is very important to extract text from these images in a simple yet effective manner. We can obtain useful information by testing these images. We aim is to summarize the visual information and retrieve its content. The Optical Recognition System involves several algorithms that fulfill this purpose. Text Extraction involves a lot of processes from text detection, localization, segmentation and, text recognition. Tesseract is the most optimized OCR Engine build by HP Labs and owned by Google. Text Detection involves the recognition of text from desired input images. Text Localization involves identifying the position of text on the images. Tesseract works pretty well on the light-colored background but unable to recognize text on darker shades. We have tried to apply various image processing techniques. This method will allow us to recognize text from most types of background. We propose to provide methods for easy text extraction. Track bar allows the user to adjust various parameters to extract a required text from an Image. This method is gaining huge importance in years to come. For Automation, we can use a set of image processing techniques such as edge detection, filtering and, blurring for better results. A series of these steps will enable us to extract text from images efficiently. This experiment compares the optimized result by two methods for efficient Text Extraction.