First study of the RuPatient health information system with optical character recognition of medical records based on machine learning

RuPatient health information system (HIS) is a computer program consisting of a doctor-patient web user interface, which includes algorithms for recognizing medical record text and entering it into the corresponding fields of the system.Aim. To evaluate the effectiveness of RuPatient HIS in actual clinical practice.Material and methods. The study involved 10 cardiologists and intensivists of the department of cardiology and сardiovascular intensive care unit of the L. A. Vorokhobov City Clinical Hospital 67 We analyzed images (scanned copies, photos) of discharge reports from patients admitted to the relevant departments in 2021. The following fields of medical documentation was recognized: Name, Complaints, Anamnesis of life and illness, Examination, Recommendations. The correctness and accuracy of recognition of entered information were analyzed. We compared the recognition quality of RuPatient HIS and a popular optical character recognition application (FineReader for Mac).Results. The study included 77 pages of discharge reports of patients from various hospitals in Russia from 50 patients (men, 52%). The mean age of patients was 57,7±7,9 years. The number of reports with correctly recognized fields in various categories using the program algorithms was distributed as follows: Name — 14 (28%), Diagnosis — 13 (26%), Complaints — 40 (80%), Anamnesis — 14 (28%), Examination — 24 (48%), Recommendations — 46 (92%). Data that was not included in the category was also recognized and entered in the comments field. The number of recognized words was 549±174,9 vs 522,4±215,6 (p=0,5), critical errors in words — 2,1±1,6 vs 4,4±2,8 (p<0,001), non-critical errors — 10,3±4,3 vs 5,6±3,3 (p<0,001) for RuPatient HIS and optical character recognition application for a personal computer, respectively.Conclusion. The developed RuPatient HIS, which includes a module for recognizing medical records and entering data into the corresponding fields, significantly increases the document management efficiency with high quality of optical character recognition based on neural network technologies and the automation of filling process.

Szerzőazonosítás Jacob és Wilhelm Grimm zajos, digitalizált levelezésében

Az alábbi cikk egy multidiszciplináris projekt eredményeit mutatja be, amely a különböző digitalizációs stratégiák számítógépes szöveganalízisben való használhatóságát járja körül. Pontosabban Jacob és Wilhelm Grimm szerzőségének automatizált megkülönböztetésére tettünk kísérletet, melyet egy HTR (HandwrittenText Recognition – kézzel írott szöveg felismerése) és OCR (Optical Character Recognition – optikai karakterfelismerés) által feldolgozott levelezéskorpuszban hajtottunk végre, korrekció nélkül – felmérve, hogy az így keletkezett zaj milyen hatással van a fivérek különböző írásmódjának azonosítására. Összegezve,úgy tűnik, hogy az OCR megbízható helyettesítője lehet a manuális átírásnak, legalábbis a szerzőazonosítás kérdéskörét illetően. Eredményeink továbbá abba az irányba mutatnak, miszerint még a különböző digitalizációs eljárásokból származó tanító- és tesztkorpuszok (training and test set) is használhatók a szerzőazonosítás során. A HTR-t tekintve a kutatás azt demonstrálja, hogy ez az automatizált átírás ugyan az OCR-hez képest szignifikánsan növeli a szövegek félrecsoportosításának veszélyét, ám körülbelül 20% feletti tisztaság már önmagában elegendő ahhoz, hogy a véletlennél nagyobb esélye legyen a helyes binárismegfeleltetésnek.

Aplikasi Mobile Money Management Dengan Fitur Optical Character Recognition Menggunakan Framework React Native

Financial management is one of the important things in the process of achieving the financial goals of a person or an organization. Everyone has their own way to manages finances, this is dependent on the character and they goals. Financial management can be done conventionally, for example by manual method which is commonly done by write the expenses, income, and savings in a notebook. However, if the note must contain details of the transactions carried out, it can be considered less efficient. The use of Optical Character Recognition will be able to answer this problem, by taking a picture of the transaction, all transaction details will be recorded on the smartphone, and the user can validate the results obtained and save the record on the smartphone user. Users can also immediately see the total transactions made according to the selected time range without having to calculate each transaction made manually. The application will be designed using the react native framework which allows it to run on various platforms.

Optical Character Recognition of Indian Language Manuscripts using Convolutional Neural Networks

India's constitution has 22 languages written in 17 different scripts. These materials have a limited lifespan, and as generations pass, these materials deteriorate, and the vital knowledge is lost. This work uses digital texts to convey information to future generations. Optical Character Recognition (OCR) helps extract information from scanned manuscripts (printed text). This paper proposes a simple and effective solution of optical character recognition (OCR) Sanskrit Character from text document images using long short-term memory (LSTM) and neural networks of Sanskrit Characters. Existing methods focuses only upon the single touching characters. But our main focus is to design a robust method using Bidirectional Long Short-Term Memory (BLSTM) architecture for overlapping lines, touching characters in middle and upper zone and half character which would increase the accuracy of the present OCR system for recognition of poorly maintained Sanskrit literature.

PENDETEKSIAN PLAT NOMOR KENDARAAN MENGGUNAKAN ALGORITMA YOU ONLY LOOK ONCE V3 DAN TESSERACT

Perkembangan teknologi saat ini sangat berkembang pesat. Teknologi yang saat ini sedang dilakukan pengembangan secara besar-besaran yaitu Artificial Intelligence. Artificial Intelligence atau AI memiliki berbagai macam fungsi dan tujuan tergantung dari sistem yang akan dibuat. Salah satunya yaitu pendekteksian objek dan teks dari gambar atau video. Contoh dari pemanfaatan teknologi ini yaitu pada pendeteksian objek dan teks pada plat nomor kendaraan. Pada penelitian ini dilakukan perancangan sistem dengan menggunakan algoritma You Only Look Once V3 sebagai algoritma pendeteksi objek dan Tesseract Optical Character Recognition sebagai pendeteksi teks dalam gambar. Perancangan ini akan dibantu dengan library OpenCV pada bahasa pemrogramanan python dan menggunakan dataset gambar yang sudah tersedia. Penelitian ini bertujuan untuk mengetahui tingkat keakurasian algoritma You Only Look Once V3 yang dikombinasikan dengan Tesseract Optical Character Recognition.

Photometric Ligature Extraction Technique for Urdu Optical Character Recognition

Urdu Optical Character Recognition (OCR) based on character level recognition (analytical approach) is less popular as compared to ligature level recognition (holistic approach) due to its added complexity, characters and strokes overlapping. This paper presents a holistic approach Urdu ligature extraction technique. The proposed Photometric Ligature Extraction (PLE) technique is independent of font size and column layout and is capable to handle non-overlapping and all inter and intra overlapping ligatures. It uses a customized photometric filter along with the application of X-shearing and padding with connected component analysis, to extract complete ligatures instead of extracting primary and secondary ligatures separately. A total of ~ 2,67,800 ligatures were extracted from scanned Urdu Nastaliq printed text images with an accuracy of 99.4%. Thus, the proposed framework outperforms the existing Urdu Nastaliq text extraction and segmentation algorithms. The proposed PLE framework can also be applied to other languages using the Nastaliq script style, languages such as Arabic, Persian, Pashto, and Sindhi.