Open Source Optical Character Recognition Research Articles

Characteristics of flattening filter free beams at low monitor unit settings

Newer linear accelerators (linacs) have been equipped to deliver flattening filter free (FFF) beams. When FFF beams are used for step-and-shoot intensity-modulated radiotherapy (IMRT), the stability of delivery of small numbers of monitor units (MU) is important. The authors developed automatic measurement techniques to evaluate the stability of the dose profile, dose linearity, and consistency. Here, the authors report the performance of the Artiste™ accelerator (Siemens, Erlangen, Germany) in delivering low-MU FFF beams. A 6 MV flattened beam (6X) with 300 MU/min dose rate and FFF beams of 7 (7XU) and 11 MV (11XU), each with a 500 MU/min dose rate, were measured at 1, 2, 3, 5, 8, 10, and 20 MU settings. For the 2000 MU/min dose rate, the 7 (7XUH) and 11 MV (11XUH) beams were set at 10, 15, 20, 25, and 30 MU because of the limits of the minimum MU settings. Beams with 20 × 20 and 10 × 10 cm(2) field sizes were alternately measured ten times in intensity modulated (IM) mode, with which Siemens linacs regulate beam delivery for step-and-shoot IMRT. The in- and crossplane beam profiles were measured using a Profiler™ Model 1170 (Sun Nuclear Corporation, Melbourne, FL) in multiframe mode. The frames of 20 × 20 cm(2) beams were identified at the off-axis profile. The 6X beam profile was normalized at the central axis. The 7 and 11 MV FFF beam profiles were rescaled to set the dose at the central axis at 145% and 170%, respectively. Point doses were also measured using a Farmer-type ionization chamber and water-equivalent solid phantom to evaluate the linearity and consistency of low-MU beam delivery. The values displayed on the electrometer were recognized with a USB-type camera and read with open-source optical character recognition software. The symmetry measurements of the 6X, 7XU, and 11XU beam profiles were better than 2% for beams ≥ 2 MU and improved with increasing MU. The variations in flatness of FFF beams ≥ 2 MU were ± 5%. The standard deviation of the symmetry and flatness also decreased with increasing MU. The linearity of the 6X beam was ± 1% and ± 2% for the beams of ≥ 5 and ≥ 3 MU, respectively. The 7XU and 11XU beams of ≥ 2 MU showed linearity with ± 2% except the 7XU beam of 8 MU (+2.9%). The profiles of the FFF beams with 2000 and 500 MU/min dose rate were similar. The characteristics of low-MU beams delivered in IM mode were evaluated using an automatic measurement system developed in this study. The authors demonstrated that the profiles of FFF beams of the Artiste™ linac were highly stable, even at low MU. The linearity of dose output was also stable for beams ≥ 2 MU.

Optical Character Recognition by Open source OCR Tool Tesseract: A Case Study

ABSTRACT Optical character recognition (OCR) method has been used in converting printed text into editable text. OCR is very useful and popular method in various applications. Accuracy of OCR can be dependent on text preprocessing and segmentation algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, complex background of image etc. We begin this paper with an introduction of Optical Character Recognition (OCR) method, History of Open Source OCR tool Tesseract, architecture of it and experiment result of OCR performed by Tesseract on different kinds images are discussed. We conclude this paper by comparative study of this tool with other commercial OCR tool Transym OCR by considering vehicle number plate as input. From vehicle number plate we tried to extract vehicle number by using Tesseract and Transym and compared these tools based on various parameters. explained.Keywords like: Desktop OCR, Server OCR, Web OCR etc.

Open source optical character recognition for historical research

PurposeThis paper aims to present an evaluation of open source OCR for supporting research on material in small‐ to medium‐scale historical archives.Design/methodology/approachThe approach was to develop a workflow engine to support the easy customisation of the OCR process towards the historical materials using open source technologies. Commercial OCR often fails to deliver sufficient results here, as their processing is optimised towards large‐scale commercially relevant collections. The approach presented here allows users to combine the most effective parts of different OCR tools.FindingsThe authors demonstrate their application and its flexibility and present two case studies, which demonstrate how OCR can be embedded into wider digitally enabled historical research. The first case study produces high‐quality research‐oriented digitisation outputs, utilizing services that the authors developed to allow for the direct linkage of digitisation image and OCR output. The second case study demonstrates what becomes possible if OCR can be customised directly within a larger research infrastructure for history. In such a scenario, further semantics can be added easily to the workflow, enhancing the research browse experience significantly.Originality/valueThere has been little work on the use of open source OCR technologies for historical research. This paper demonstrates that the authors' workflow approach allows users to combine commercial engines' ability to read a wider range of character sets with the flexibility of open source tools in terms of customisable pre‐processing and layout analysis. All this can be done without the need to develop dedicated code.

Quantitative Computed Tomography (QCT) as a Radiology Reporting Tool by Using Optical Character Recognition (OCR) and 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. This reporting system was created as a development tool by using an open-source OCR software and an open-source macro program. The main module was designed for OCR to report QCT images in radiology reading process. 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 accuracy test of OCR was performed on randomly selected QCTs. QCT as a radiology reporting tool successfully acted as OCR of QCT. The diagnosis of normal, osteopenia, or osteoporosis is also determined. Error correction of OCR is done with AutoHotkey-coded module. The results of T scores of femoral neck and lumbar vertebrae had an accuracy of 100 and 95.4 %, respectively. A convenient QCT reporting system could be established by utilizing open-source OCR software and open-source macro program. This method can be easily adapted for other QCT applications and PACS/EMR.

Automated Extraction of Radiation Dose Information for CT Examinations

Exposure to radiation as a result of medical imaging is currently in the spotlight, receiving attention from Congress as well as the lay press. Although scanner manufacturers are moving toward including effective dose information in the Digital Imaging and Communications in Medicine headers of imaging studies, there is a vast repository of retrospective CT data at every imaging center that stores dose information in an image-based dose sheet. As such, it is difficult for imaging centers to participate in the ACR's Dose Index Registry. The authors have designed an automated extraction system to query their PACS archive and parse CT examinations to extract the dose information stored in each dose sheet. First, an open-source optical character recognition program processes each dose sheet and converts the information to American Standard Code for Information Interchange (ASCII) text. Each text file is parsed, and radiation dose information is extracted and stored in a database which can be queried using an existing pathology and radiology enterprise search tool. Using this automated extraction pipeline, it is possible to perform dose analysis on the >800,000 CT examinations in the PACS archive and generate dose reports for all of these patients. It is also possible to more effectively educate technologists, radiologists, and referring physicians about exposure to radiation from CT by generating report cards for interpreted and performed studies. The automated extraction pipeline enables compliance with the ACR's reporting guidelines and greater awareness of radiation dose to patients, thus resulting in improved patient care and management.

TH-C-201B-11: Automated Extraction and Reporting of Dose Information from Computed Tomography Examinations

PURPOSE: Exposure to radiation as a result of medical imaging is currently in the spotlight receiving attention from Congress as well as the lay press. While scanner manufacturers are moving towards including effective doseinformation in the DICOM headers of imaging studies there is a vast repository of retrospective CT data at every imaging center which stores doseinformation in a patient protocol image. As such it is difficult for imaging centers to participate in the American College of Radiology (ACR) Dose Registry. METHOD AND MATERIALS: We have designed an automated extraction system to query our PACS archive and parse our CT examinations to extract doseinformation stored in each patient protocol image. First an open‐source optical character recognition (OCR) program processes each protocol image and converts the information to ASCII text. We parse each ASCII file and extract radiationdoseinformation which is stored in a database and queried using an existing pathology and radiology enterprise search tool at our institution. We conducted a validation study to compare the extracted text information with the original patient protocol image. 2108 CT examinations performed at our institution between 2003 and 2010 were randomly selected and processed. Two board‐certified radiologists reviewed the extracted radiationdoseinformation and compared it with the patient protocol image of each study. RESULTS: The pipeline extracts and calculates total DLP with an accuracy of 100%. CONCLUSION: Using this automated extraction pipeline we are able to perform dose analysis on the more than 869000 CT examinations in our PACS archive and generate dose report cards for every patient scanned at our institution. The automated extraction pipeline allows us to be compliant with the ACR reporting guidelines and be more cognizant of radiationdose to our patients thus resulting in improved patient care and management. CONFLICT OF INTEREST: None.