1. PROCESSING IMAGES OF SALES RECEIPTS FOR ISOLATING AND RECOGNISING TEXT INFORMATION
- Author
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A. S. Nazdryukhin, A. N. Tushev, and I. N. Khramtsov
- Subjects
Technology ,Morphological gradient ,Artificial neural network ,Point (typography) ,Computer science ,business.industry ,sales receipts ,020207 software engineering ,Pattern recognition ,Image processing ,02 engineering and technology ,Mathematical morphology ,neural networks ,Image conversion ,image processing ,ocr ,image analysis ,0202 electrical engineering, electronic engineering, information engineering ,ComputingMethodologies_DOCUMENTANDTEXTPROCESSING ,020201 artificial intelligence & image processing ,Tesseract ,Artificial intelligence ,Closing (morphology) ,business - Abstract
Objectives. This article presents an application for the processing of scanned images of sales receipts for subsequent extraction of text information using the Tesseract OCR Engine. Such an application is useful for maintaining a family budget or for accounting in small companies. The main problem of receipt recognition is the low quality of ink and printing paper, which results in creasing and tears, as well as the rapid fading of printed characters.Methods. The study is based on a number of algorithms based on mathematical morphology methods for opening, closing and morphological gradient operations, as well as image conversion, which can significantly improve the final recognition of characters by Tesseract.Results. In order to solve this problem, a special image normalisation algorithm is proposed, which includes locating a receipt on an image, processing the received image section, removing image capture and carrier defects, as well as point processing for restoring missing characters. The developed application supports increased recognition accuracy of text information when using Tesseract OCR.Conclusion. The developed system recognises characters with fairly high accuracy, demonstrates a result that is better than that obtained when using the unmodified Tesseract method, but which is still inferior to the recognition accuracy of ABBY FineReader. Methods are also been proposed aimed at improving the developed algorithm.
- Published
- 2020