Optical Character Recognition on images with colorful background

Brisinello, Matteo; Grbić, Ratko; Stefanovic, Dejan; Peckai-Kovac, Robert

doi:10.1109/icce-berlin.2018.8576202

Cited by 14 publications

(4 citation statements)

References 5 publications

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“…The text is stored in local storage, and there may bea chance of data loss when the app is uninstalled or if the device is crashed. [7], [8] These papers have researched an android application capable of live translation of the text by accessing each frame from the device's camera. This application is bounded to a small set of users willing to read the text in their local language.…”

Section: IImentioning

confidence: 99%

Text Interpreter & Converter

Vasudev¹

2023

IJRASET

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With the exponential growth in data, it is essential to analyze it. The data is a valuable source ofinformation and knowledge that should be effectivelyinterpreted to be helpful. The data should be interpreted in different forms based on the user requirements to get precise information and knowledge. Nowadays smartphones are the most commonly used electronic device. A smartphone is notonly a communication device, it is also a powerful computing device. So it is possible to apply translation, text extraction, summary, and much moretechniques, which require much computational work. This paper presents an application to analyze the text from documents on smartphones. However, it is challenging to interpret the documents onsmartphones. The proposed application converts the documents or images to searchable and editable digital text, and further, it can be used to analyse them into different forms. The objective of this application is four-fold 1) To recognize text from documents orimages by using optical Character Recognition 2) Summarization of the text 3) Translate the extracted text to different languages 4) Generation of speech from the text by using a text-to-speech algorithm.

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Section: IImentioning

confidence: 99%

Text Interpreter & Converter

Vasudev¹

2023

IJRASET

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“…Further, there had been many researches to improve the performance of OCR and from them, Matteo Brisinello and Tan Chiang Wei came up with different techniques and performance rates. In the research by Matteo Brisinello et al [5] a system was proposed that improved the performance of Tesseract Optical Character Recognition (OCR). It mainly uses images with colourful backgrounds.…”

Section: Surveymentioning

confidence: 99%

Text Extraction and Language Translation for Health Care and Clinical Records Using Deep Learning

Kayalvizhi¹,

Amena²,

S³

et al. 2022

Preprint

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Language conversion is a technique that has tremendous use worldwide. With the multitude of languages available, it is not feasible for an average person to learn languages he/she will not use. However, everyone faces situations where knowing a certain language would help in a certain city or even during work. This problem can be solved using the latest technologies in deep learning and computer vision. A system that can identify the text from images and convert them into a well-known language like English has tremendous use everywhere especially in the medical field. The medical field is one where communication and exchange of information is critical. In case of a medical emergency during a tourists visit, the inability to understand what the doctor is trying to say or what type of medication is being prescribed would lead only to more stress and panic. There are systems that have tried to solve this problem and there will be more in the future. One of the glaring issues faced by contemporary systems is the lack of image processing before getting the text of a signboard using computer vision algorithms. Blurred images or images where the characters of the text are too close to each other can hamper the system’s performance to a great extent. The proposed system provides a way by which the text present in an image can be extracted and displayed back to the user in a universal language like English. The system incorporates an optical character recognition tool called pytesseract to get the text from the image and it is converted into a different language using deep learning.

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“…The purpose of input images preprocessing is to improve the accuracy of text detection and recognition. This stage includes, for example, contrasting, colored background removal [27], binarization [28], as well as removing image defects (noise, glare, overlaying holograms) using various types of filtering and the use of morphological operations [29].…”

Section: Scopementioning

confidence: 99%

Weighted combination of per-frame recognition results for text recognition in a video stream

Petrova¹,

Bulatov

Arlazarov³

2021

Computer Optics

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The scope of uses of automated document recognition has extended and as a result, recognition techniques that do not require specialized equipment have become more relevant. Among such techniques, document recognition using mobile devices is of interest. However, it is not always possible to ensure controlled capturing conditions and, consequentially, high quality of input images. Unlike specialized scanners, mobile cameras allow using a video stream as an input, thus obtaining several images of the recognized object, captured with various characteristics. In this case, a problem of combining the information from multiple input frames arises. In this paper, we propose a weighing model for the process of combining the per-frame recognition results, two approaches to the weighted combination of the text recognition results, and two weighing criteria. The effectiveness of the proposed approaches is tested using datasets of identity documents captured with a mobile device camera in different conditions, including perspective distortion of the document image and low lighting conditions. The experimental results show that the weighting combination can improve the text recognition result quality in the video stream, and the per-character weighting method with input image focus estimation as a base criterion allows one to achieve the best results on the datasets analyzed.

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Optical Character Recognition on images with colorful background

Cited by 14 publications

References 5 publications

Text Interpreter & Converter

Text Interpreter & Converter

Text Extraction and Language Translation for Health Care and Clinical Records Using Deep Learning

Weighted combination of per-frame recognition results for text recognition in a video stream

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