In the past three years, the results of numerous excellent and rigorous studies of OCR system typeset-character accuracy (henceforth OCR accuracy) have been published, encouraging performance comparisons between a variety of OCR products and technologies. This method can be easily adapted for other QCT applications and PACS/EMR.įrequently object recognition accuracy is a key component in the performance analysis of pattern matching systems.
#NASA OCR FONT SOFTWARE#
A convenient QCT reporting system could be established by utilizing open-source OCR software and open-source macro program.
The results of T scores of femoral neck and lumbar vertebrae had an accuracy of 100 and 95.4 %, respectively. Error correction of OCR is done with AutoHotkey-coded module. The diagnosis of normal, osteopenia, or osteoporosis is also determined. QCT as a radiology reporting tool successfully acted as OCR of QCT. The accuracy test of OCR was performed on randomly selected QCTs. The principal processes are as follows: (1) to save a QCT report as a graphic file, (2) to recognize the characters from an image as a text, (3) to extract the T scores from the text, (4) to perform error correction, (5) to reformat the values into QCT radiology reporting template, and (6) to paste the reports into the electronic medical record (EMR) or picture archiving and communicating system (PACS). The main module was designed for OCR to report QCT images in radiology reading process. This reporting system was created as a development tool by using an open-source OCR software and an open-source macro program. The objectives are (1) to introduce a new concept of making a quantitative computed tomography (QCT) reporting system by using optical character recognition ( OCR) and macro program and (2) to illustrate the practical usages of the QCT reporting system in radiology reading environment. Lee, Young Han Song, Ho-Taek Suh, Jin-Suck Quantitative computed tomography (QCT) as a radiology reporting tool by using optical character recognition ( OCR) and macro program. This includes a popular open source OCR engine named Tesseract for text detection & recognition and Flite speech synthesis module, for adding text-to-speech ability. In this work we developed a complete OCR framework with subsystems from open source desktop community. Keeping this in perspective we propose a complete text detection and recognition system with speech synthesis ability, using existing desktop technology. For instance currently there are many open source OCR engines for desktop platform but, to our knowledge, none are available on mobile platform. However with all these advancements we find very few open source software available for mobile phones. These applications help people quickly gather the information in digital format and interpret them without the need of carrying laptops or tablet PCs. Exciting new social applications are emerging on mobile landscape, like, business card readers, sing detectors and translators. Today most new mobile phones are capable of capturing images, recording video, and browsing internet and do much more. Mobile phones have evolved from passive one-to-one communication device to powerful handheld computing device. Zhou, Steven Zhiying Gilani, Syed Omer Winkler, Stefan Open source OCR framework using mobile devices
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