Paper bills can be prone to physical damage, fading, and loss, resulting in the loss of important financial records and difficulties retrieving necessary information. This paper introduces a system designed to automatically extract and store textual data from images of bills, utilizing image processing techniques through OpenCV for bill identification and preprocessing alongside Tesseract OCR for precise text recognition. Chief methods used include the Canny edge detector to detect edges, contour detection to isolate the bill as the big rectangular figure, adaptive thresholding to change the image into a distinct black-and-white picture, and text segmentation to separate lines, words, and letters. The data, in an orderly format, is subsequently retrieved and dealt with efficiently. Adapting to handwriting-based bills presents massive challenges, which include dealing with irregular text flow, potential OCR errors, and data privacy issues. The results of this research improve the preservation and accessibility of financial records, and continuing work will look toward utilizing more advanced OCR systems and deep learning methods to improve accuracy.