IMPROVING THE EFFICIENCY OF TESSERACT OCR ENGINE

This project investigates the principles of optical character recognition used in the Tesseract OCR engine and techniques to improve its efficiency and runtime. Optical character recognition (OCR) method has been used in converting printed text into editable text in various applications over a variety of devices such as Scanners, computers, tablets etc. But now Mobile is taking over the computer in all the domains but OCR still remains one not so conquered field. So programmers need to improve the efficiency of the OCR system to make it run properly on Mobile devices. This paper focuses on improving the Tesseract OCR efficiency for Hindi language to run on Mobile devices as there a not many applications for the same and most of them are either not open source or not for mobile devices. Improving Hindi text extraction will increase Tesseract\u27s performance for Mobile phone apps and in turn will draw developers to contribute towards Hindi OCR . This paper presents a preprocessing technique being applied to the Tesseract Engine to improve the recognition of the characters keeping the runtime low. Hence the system runs smoothly and efficiently on mobile devices(Android) as it does on the bigger machines

Skip Trie Matching for Real-Time OCR Output Error Corrrection on Smartphones

Many Visually Impaired individuals are managing their daily activities with the help of smartphones. While there are many vision-based mobile applications to identify products, there is a relative dearth of applications for extracting useful nutrition information. In this report, we study the performance of existing OCR systems available for the Android platform, and choose the best to extract the nutrition facts information from U.S grocery store packages. We then provide approaches to improve the results of text strings produced by the Tesseract OCR engine on image segments of nutrition tables automatically extracted by an Android 2.3.6 smartphone application using real-time video streams of grocery products. We also present an algorithm, called Skip Trie Matching (STM), for real-time OCR output error correction on smartphones. The algorithm’s performance is compared with Apache Lucene’s spell checker. Our evaluation indicates that the average run time of the STM algorithm is lower than Lucene’s. (68 pages

Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate

Optical character recognition (OCR) is a technology to digitize a paper-based document to digital form. This research studies the extraction of the characters from a Thai vehicle registration certificate via a Google Cloud Vision API and a Tesseract OCR. The recognition performance of both OCR APIs is also examined. The 84 color image files comprised three image sizes/resolutions and five image characteristics. For suitable image type comparison, the greyscale and binary image are converted from color images. Furthermore, the three pre-processing techniques, sharpening, contrast adjustment, and brightness adjustment, are also applied to enhance the quality of image before applying the two OCR APIs. The recognition performance was evaluated in terms of accuracy and readability. The results showed that the Google Cloud Vision API works well for the Thai vehicle registration certificate with an accuracy of 84.43%, whereas the Tesseract OCR showed an accuracy of 47.02%. The highest accuracy came from the color image with 1024×768 px, 300dpi, and using sharpening and brightness adjustment as pre-processing techniques. In terms of readability, the Google Cloud Vision API has more readability than the Tesseract. The proposed conditions facilitate the possibility of the implementation for Thai vehicle registration certificate recognition system