The Experts below are selected from a list of 12333 Experts worldwide ranked by ideXlab platform
Jiao Sun - One of the best experts on this subject based on the ideXlab platform.
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FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) Systems
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Lu Chen, Jiao SunAbstract:Deep neural networks (DNNs) significantly improved the accuracy of Optical Character Recognition (OCR) and inspired many important applications. Unfortunately, OCRs also inherit the vulnerabilities of DNNs under adversarial examples. Different from colorful vanilla images, text images usually have clear backgrounds. Adversarial examples generated by most existing adversarial attacks are unnatural and pollute the background severely. To address this issue, we propose the Fast Adversarial Watermark Attack (FAWA) against sequence-based OCR models in the white-box manner. By disguising the perturbations as watermarks, we can make the resulting adversarial images appear natural to human eyes and achieve a perfect attack success rate. FAWA works with either gradient-based or optimization-based perturbation generation. In both letter-level and word-level attacks, our experiments show that in addition to natural appearance, FAWA achieves a 100% attack success rate with 60% less perturbations and 78% fewer iterations on average. In addition, we further extend FAWA to support full-color watermarks, other languages, and even the OCR accuracy-enhancing mechanism.
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fawa fast adversarial watermark attack on Optical Character Recognition ocr systems
European conference on Machine Learning, 2020Co-Authors: Lu Chen, Jiao SunAbstract:Deep neural networks (DNNs) significantly improved the accuracy of Optical Character Recognition (OCR) and inspired many important applications. Unfortunately, OCRs also inherit the vulnerability of DNNs under adversarial examples. Different from colorful vanilla images, text images usually have clear backgrounds. Adversarial examples generated by most existing adversarial attacks are unnatural and pollute the background severely. To address this issue, we propose the F ast Adversarial Watermark Attack (FAWA) against sequence-based OCR models in the white-box manner. By disguising the perturbations as watermarks, we can make the resulting adversarial images appear natural to human eyes and achieve a perfect attack success rate. FAWA works with either gradient-based or optimization-based perturbation generation. In both letter-level and word-level attacks, our experiments show that in addition to natural appearance, FAWA achieves a 100% attack success rate with 60% less perturbations and 78% fewer iterations on average. In addition, we further extend FAWA to support full-color watermarks, other languages, and even the OCR accuracy-enhancing mechanism.
Amit Bhatt - One of the best experts on this subject based on the ideXlab platform.
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application of Optical Character Recognition with natural language processing for large scale quality metric data extraction in colonoscopy reports
Gastrointestinal Endoscopy, 2021Co-Authors: Sobia N Laique, Umar Hayat, Shashank Sarvepalli, Byron P Vaughn, Mounir Ibrahim, John Mcmichael, Kanza Noor Qaiser, Carol A Burke, Amit BhattAbstract:Abstract Background and Aims Colonoscopy is commonly performed for colorectal cancer screening in the United States. Reports are often generated in a non-standardized format and are not always integrated into electronic health records. Thus, this information is not readily available for streamlining quality management, participating in endoscopy registries, or reporting of patient- and center-specific risk factors predictive of outcomes. We aim to demonstrate the use of a new hybrid approach using natural language processing of charts that have been elucidated with Optical Character Recognition processing (OCR/NLP hybrid) to obtain relevant clinical information from scanned colonoscopy and pathology reports, a technology co-developed by Cleveland Clinic and eHealth Technologies (West Henrietta, NY, USA). Methods This was a retrospective study conducted at Cleveland Clinic, Cleveland, Ohio, and the University of Minnesota, Minneapolis, Minnesota. A randomly sampled list of outpatient screening colonoscopy procedures and pathology reports was selected. Desired variables were then collected. Two researchers first manually reviewed the reports for the desired variables. Then, the OCR/NLP algorithm was used to obtain the same variables from 3 electronic health records in use at our institution: Epic (Verona, Wisc, USA), ProVation (Minneapolis, Minn, USA) used for endoscopy reporting, and Sunquest PowerPath (Tucson, Ariz, USA) used for pathology reporting. Results Compared with manual data extraction, the accuracy of the hybrid OCR/NLP approach to detect polyps was 95.8%, adenomas 98.5%, sessile serrated polyps 99.3%, advanced adenomas 98%, inadequate bowel preparation 98.4%, and failed cecal intubation 99%. Comparison of the dataset collected via NLP alone with that collected using the hybrid OCR/NLP approach showed that the accuracy for almost all variables was >99%. Conclusions Our study is the first to validate the use of a unique hybrid OCR/NLP technology to extract desired variables from scanned procedure and pathology reports contained in image format with an accuracy >95%.
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application of Optical Character Recognition with natural language processing for large scale quality metric data extraction in colonoscopy reports
Gastrointestinal Endoscopy, 2020Co-Authors: Sobia N Laique, Umar Hayat, Shashank Sarvepalli, Byron P Vaughn, Mounir Ibrahim, John Mcmichael, Kanza Noor Qaiser, Carol A Burke, Amit BhattAbstract:Abstract Background and Aims Colonoscopy is commonly performed for colorectal cancer screening in the United States. Reports are often generated in a nonstandardized format and are not always integrated into electronic health records. Thus, this information is not readily available for streamlining quality management, participating in endoscopy registries, or reporting of patient and center-specific risk factors predictive of outcomes. We aim to demonstrate the use of a new hybrid approach using natural language processing of charts that have been elucidated with Optical Character Recognition processing (OCR/NLP hybrid) to obtain relevant clinical information from scanned colonoscopy and pathology reports, a technology co-developed by Cleveland Clinic and eHealth Technologies (West Henrietta, NY, USA). Methods This was a retrospective study conducted at Cleveland Clinic, Cleveland, Ohio, and the University of Minnesota, Minneapolis, Minnesota. A randomly sampled list of outpatient screening colonoscopy procedures and pathology reports was selected. Desired variables were then collected. Two researchers first manually reviewed the reports for the desired variables. Then the OCR/NLP algorithm was used to obtain the same variables from 3 EHRs being used at our institution: Epic (Verona, Wisc, USA), ProVation (Minneapolis, Minn, USA) used for endoscopy reporting, and Sunquest PowerPath (Tucson, Ariz, USA) used for pathology reporting. Results When compared with manual data extraction, the hybrid OCR/NLP approach was able to detect polyps with an accuracy of 95.8%, adenomas 98.5%, sessile serrated polyps 99.3%, advanced adenomas 98%, inadequate bowel preparation 98.4% and failed cecal intubation 99%. Upon comparison of the data set collected via NLP alone with that collected using the hybrid OCR/NLP approach, the accuracy for almost all variables was greater than 99%. Conclusions Our study is the first to validate the use of a unique hybrid OCR/NLP technology to extract desired variables from the scanned procedure and pathology reports contained in image format with an accuracy of greater than 95%.
Lu Chen - One of the best experts on this subject based on the ideXlab platform.
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FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) Systems
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Lu Chen, Jiao SunAbstract:Deep neural networks (DNNs) significantly improved the accuracy of Optical Character Recognition (OCR) and inspired many important applications. Unfortunately, OCRs also inherit the vulnerabilities of DNNs under adversarial examples. Different from colorful vanilla images, text images usually have clear backgrounds. Adversarial examples generated by most existing adversarial attacks are unnatural and pollute the background severely. To address this issue, we propose the Fast Adversarial Watermark Attack (FAWA) against sequence-based OCR models in the white-box manner. By disguising the perturbations as watermarks, we can make the resulting adversarial images appear natural to human eyes and achieve a perfect attack success rate. FAWA works with either gradient-based or optimization-based perturbation generation. In both letter-level and word-level attacks, our experiments show that in addition to natural appearance, FAWA achieves a 100% attack success rate with 60% less perturbations and 78% fewer iterations on average. In addition, we further extend FAWA to support full-color watermarks, other languages, and even the OCR accuracy-enhancing mechanism.
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fawa fast adversarial watermark attack on Optical Character Recognition ocr systems
European conference on Machine Learning, 2020Co-Authors: Lu Chen, Jiao SunAbstract:Deep neural networks (DNNs) significantly improved the accuracy of Optical Character Recognition (OCR) and inspired many important applications. Unfortunately, OCRs also inherit the vulnerability of DNNs under adversarial examples. Different from colorful vanilla images, text images usually have clear backgrounds. Adversarial examples generated by most existing adversarial attacks are unnatural and pollute the background severely. To address this issue, we propose the F ast Adversarial Watermark Attack (FAWA) against sequence-based OCR models in the white-box manner. By disguising the perturbations as watermarks, we can make the resulting adversarial images appear natural to human eyes and achieve a perfect attack success rate. FAWA works with either gradient-based or optimization-based perturbation generation. In both letter-level and word-level attacks, our experiments show that in addition to natural appearance, FAWA achieves a 100% attack success rate with 60% less perturbations and 78% fewer iterations on average. In addition, we further extend FAWA to support full-color watermarks, other languages, and even the OCR accuracy-enhancing mechanism.
Shashank Sarvepalli - One of the best experts on this subject based on the ideXlab platform.
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application of Optical Character Recognition with natural language processing for large scale quality metric data extraction in colonoscopy reports
Gastrointestinal Endoscopy, 2021Co-Authors: Sobia N Laique, Umar Hayat, Shashank Sarvepalli, Byron P Vaughn, Mounir Ibrahim, John Mcmichael, Kanza Noor Qaiser, Carol A Burke, Amit BhattAbstract:Abstract Background and Aims Colonoscopy is commonly performed for colorectal cancer screening in the United States. Reports are often generated in a non-standardized format and are not always integrated into electronic health records. Thus, this information is not readily available for streamlining quality management, participating in endoscopy registries, or reporting of patient- and center-specific risk factors predictive of outcomes. We aim to demonstrate the use of a new hybrid approach using natural language processing of charts that have been elucidated with Optical Character Recognition processing (OCR/NLP hybrid) to obtain relevant clinical information from scanned colonoscopy and pathology reports, a technology co-developed by Cleveland Clinic and eHealth Technologies (West Henrietta, NY, USA). Methods This was a retrospective study conducted at Cleveland Clinic, Cleveland, Ohio, and the University of Minnesota, Minneapolis, Minnesota. A randomly sampled list of outpatient screening colonoscopy procedures and pathology reports was selected. Desired variables were then collected. Two researchers first manually reviewed the reports for the desired variables. Then, the OCR/NLP algorithm was used to obtain the same variables from 3 electronic health records in use at our institution: Epic (Verona, Wisc, USA), ProVation (Minneapolis, Minn, USA) used for endoscopy reporting, and Sunquest PowerPath (Tucson, Ariz, USA) used for pathology reporting. Results Compared with manual data extraction, the accuracy of the hybrid OCR/NLP approach to detect polyps was 95.8%, adenomas 98.5%, sessile serrated polyps 99.3%, advanced adenomas 98%, inadequate bowel preparation 98.4%, and failed cecal intubation 99%. Comparison of the dataset collected via NLP alone with that collected using the hybrid OCR/NLP approach showed that the accuracy for almost all variables was >99%. Conclusions Our study is the first to validate the use of a unique hybrid OCR/NLP technology to extract desired variables from scanned procedure and pathology reports contained in image format with an accuracy >95%.
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application of Optical Character Recognition with natural language processing for large scale quality metric data extraction in colonoscopy reports
Gastrointestinal Endoscopy, 2020Co-Authors: Sobia N Laique, Umar Hayat, Shashank Sarvepalli, Byron P Vaughn, Mounir Ibrahim, John Mcmichael, Kanza Noor Qaiser, Carol A Burke, Amit BhattAbstract:Abstract Background and Aims Colonoscopy is commonly performed for colorectal cancer screening in the United States. Reports are often generated in a nonstandardized format and are not always integrated into electronic health records. Thus, this information is not readily available for streamlining quality management, participating in endoscopy registries, or reporting of patient and center-specific risk factors predictive of outcomes. We aim to demonstrate the use of a new hybrid approach using natural language processing of charts that have been elucidated with Optical Character Recognition processing (OCR/NLP hybrid) to obtain relevant clinical information from scanned colonoscopy and pathology reports, a technology co-developed by Cleveland Clinic and eHealth Technologies (West Henrietta, NY, USA). Methods This was a retrospective study conducted at Cleveland Clinic, Cleveland, Ohio, and the University of Minnesota, Minneapolis, Minnesota. A randomly sampled list of outpatient screening colonoscopy procedures and pathology reports was selected. Desired variables were then collected. Two researchers first manually reviewed the reports for the desired variables. Then the OCR/NLP algorithm was used to obtain the same variables from 3 EHRs being used at our institution: Epic (Verona, Wisc, USA), ProVation (Minneapolis, Minn, USA) used for endoscopy reporting, and Sunquest PowerPath (Tucson, Ariz, USA) used for pathology reporting. Results When compared with manual data extraction, the hybrid OCR/NLP approach was able to detect polyps with an accuracy of 95.8%, adenomas 98.5%, sessile serrated polyps 99.3%, advanced adenomas 98%, inadequate bowel preparation 98.4% and failed cecal intubation 99%. Upon comparison of the data set collected via NLP alone with that collected using the hybrid OCR/NLP approach, the accuracy for almost all variables was greater than 99%. Conclusions Our study is the first to validate the use of a unique hybrid OCR/NLP technology to extract desired variables from the scanned procedure and pathology reports contained in image format with an accuracy of greater than 95%.
Sobia N Laique - One of the best experts on this subject based on the ideXlab platform.
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application of Optical Character Recognition with natural language processing for large scale quality metric data extraction in colonoscopy reports
Gastrointestinal Endoscopy, 2021Co-Authors: Sobia N Laique, Umar Hayat, Shashank Sarvepalli, Byron P Vaughn, Mounir Ibrahim, John Mcmichael, Kanza Noor Qaiser, Carol A Burke, Amit BhattAbstract:Abstract Background and Aims Colonoscopy is commonly performed for colorectal cancer screening in the United States. Reports are often generated in a non-standardized format and are not always integrated into electronic health records. Thus, this information is not readily available for streamlining quality management, participating in endoscopy registries, or reporting of patient- and center-specific risk factors predictive of outcomes. We aim to demonstrate the use of a new hybrid approach using natural language processing of charts that have been elucidated with Optical Character Recognition processing (OCR/NLP hybrid) to obtain relevant clinical information from scanned colonoscopy and pathology reports, a technology co-developed by Cleveland Clinic and eHealth Technologies (West Henrietta, NY, USA). Methods This was a retrospective study conducted at Cleveland Clinic, Cleveland, Ohio, and the University of Minnesota, Minneapolis, Minnesota. A randomly sampled list of outpatient screening colonoscopy procedures and pathology reports was selected. Desired variables were then collected. Two researchers first manually reviewed the reports for the desired variables. Then, the OCR/NLP algorithm was used to obtain the same variables from 3 electronic health records in use at our institution: Epic (Verona, Wisc, USA), ProVation (Minneapolis, Minn, USA) used for endoscopy reporting, and Sunquest PowerPath (Tucson, Ariz, USA) used for pathology reporting. Results Compared with manual data extraction, the accuracy of the hybrid OCR/NLP approach to detect polyps was 95.8%, adenomas 98.5%, sessile serrated polyps 99.3%, advanced adenomas 98%, inadequate bowel preparation 98.4%, and failed cecal intubation 99%. Comparison of the dataset collected via NLP alone with that collected using the hybrid OCR/NLP approach showed that the accuracy for almost all variables was >99%. Conclusions Our study is the first to validate the use of a unique hybrid OCR/NLP technology to extract desired variables from scanned procedure and pathology reports contained in image format with an accuracy >95%.
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application of Optical Character Recognition with natural language processing for large scale quality metric data extraction in colonoscopy reports
Gastrointestinal Endoscopy, 2020Co-Authors: Sobia N Laique, Umar Hayat, Shashank Sarvepalli, Byron P Vaughn, Mounir Ibrahim, John Mcmichael, Kanza Noor Qaiser, Carol A Burke, Amit BhattAbstract:Abstract Background and Aims Colonoscopy is commonly performed for colorectal cancer screening in the United States. Reports are often generated in a nonstandardized format and are not always integrated into electronic health records. Thus, this information is not readily available for streamlining quality management, participating in endoscopy registries, or reporting of patient and center-specific risk factors predictive of outcomes. We aim to demonstrate the use of a new hybrid approach using natural language processing of charts that have been elucidated with Optical Character Recognition processing (OCR/NLP hybrid) to obtain relevant clinical information from scanned colonoscopy and pathology reports, a technology co-developed by Cleveland Clinic and eHealth Technologies (West Henrietta, NY, USA). Methods This was a retrospective study conducted at Cleveland Clinic, Cleveland, Ohio, and the University of Minnesota, Minneapolis, Minnesota. A randomly sampled list of outpatient screening colonoscopy procedures and pathology reports was selected. Desired variables were then collected. Two researchers first manually reviewed the reports for the desired variables. Then the OCR/NLP algorithm was used to obtain the same variables from 3 EHRs being used at our institution: Epic (Verona, Wisc, USA), ProVation (Minneapolis, Minn, USA) used for endoscopy reporting, and Sunquest PowerPath (Tucson, Ariz, USA) used for pathology reporting. Results When compared with manual data extraction, the hybrid OCR/NLP approach was able to detect polyps with an accuracy of 95.8%, adenomas 98.5%, sessile serrated polyps 99.3%, advanced adenomas 98%, inadequate bowel preparation 98.4% and failed cecal intubation 99%. Upon comparison of the data set collected via NLP alone with that collected using the hybrid OCR/NLP approach, the accuracy for almost all variables was greater than 99%. Conclusions Our study is the first to validate the use of a unique hybrid OCR/NLP technology to extract desired variables from the scanned procedure and pathology reports contained in image format with an accuracy of greater than 95%.
