Relevant bibliographies by topics / Tesseract OCR

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Tesseract OCR'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Tesseract OCR"

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Dupent, Sébastien. "Tesseract-OCR." Revue Cyber & Conformité N° 2, no. 2 (2021): 23–24. http://dx.doi.org/10.3917/cyco.002.0025.

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Baruah, Priyankush Kaushik, and Dr Pranabjyoti Haloi. "Development and Implementation of a Custom License Plate Detection and Recognition System Using YOLOv10 and Tesseract OCR: A Comprehensive Study in Computer Vision and Optical Character Recognition Technologies." International Journal of Innovative Technology and Exploring Engineering 14, no. 6 (2025): 20–26. https://doi.org/10.35940/ijitee.e1083.14060525.

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This study presents an automated license plate detection and recognition system, combining YOLOv10 for Realtime object detection and Tesseract OCR for robust text extraction. The methodology involves training a customised YOLOv10 model on annotated vehicle datasets to localize license plates, followed by region-of-interest (ROI) filtering to enhance accuracy. Detected plates are processed with Tesseract OCR to convert visual data into machine-readable text. Evaluated using precision, recall, and inference speed metrics, the system achieves 97 Parsant detection accuracy and real-time performanc
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Chesley, Emily, Jillian Marcantonio, and Abigail Pearson. "Towards Syriac Digital Corpora: Evaluation of Tesseract 4.0 for Syriac OCR." Hugoye: Journal of Syriac Studies 22, no. 1 (2019): 109–92. http://dx.doi.org/10.31826/hug-2019-220105.

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Abstract This paper summarizes the results of an extensive test of Tesseract 4.0, an open-source Optical Character Recognition (OCR) engine with Syriac capabilities, and ascertains the current state of Syriac OCR technology. Three popular print types (S14, W64, and E22) representing the Syriac type styles Estrangela, Serto, and East Syriac were OCRed using Tesseract’s two different OCR modes (Syriac Language and Syriac Script). Handwritten manuscripts were also preliminarily tested for OCR. The tests confirm that Tesseract 4.0 may be relied upon for printed Estrangela texts but should be used
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Patience, Okechukwu Ogochukwu, Eziechina Malachy Amaechi, Onyemachi George, and Onuwa Nnachi Isaac. "Enhanced Text Recognition in Images Using Tesseract OCR within the Laravel Framework." Asian Journal of Research in Computer Science 17, no. 9 (2024): 58–69. http://dx.doi.org/10.9734/ajrcos/2024/v17i9499.

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This research explores the integration of Tesseract OCR (Optical Character Recognition) within the Laravel framework to enhance text recognition capabilities in images. Tesseract OCR, an open-source OCR engine, is renowned for its accuracy and efficiency in converting various image formats into editable and searchable text. However, leveraging its full potential within a robust web application framework presents unique challenges and opportunities. This implementation focuses on creating a seamless, user-friendly application that processes images uploaded by users and accurately extracts text
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Joshi, Kartik. "Study of Tesseract OCR." GLS KALP: Journal of Multidisciplinary Studies 1, no. 2 (2024): 41–50. http://dx.doi.org/10.69974/glskalp.01.02.54.

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In the current Internet and Digitization era, a huge amount of information is available in different forms like books, newspapers, etc. To preserve the contents of such documents, these documents are converted to a digital format by scanning them as images. Detection of text from the scanned images and correct identification of characters is a challenging problem in such cases. Tesseract is a recognition engine based upon open source license which uses some novel techniques for optical character recognition. Tesseract has been designed to recognize more than 100 languages. Few of these languag
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Tiwari, Anurag. "Data Extraction from Images through OCR." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 435–37. http://dx.doi.org/10.22214/ijraset.2021.37377.

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The paperwork used in maintaining various types of documents in our daily lives is tiresome and inefficient, it consumes a lot of time and it is difficult to maintain and remember the concerned documents. This project provides a solution to these problems by introducing Optical Character Recognition Technology (OCR) which runs on Tesseract OCR Engine. The project specifically aims at increasing data accessibility, usability and improving customer experience by decreasing the time spent to process, save, and maintain user data. Another objective of this project is to nullify the human error, wh
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Benaissa, Ali, Abdelkhalak Bahri, Ahmad El Allaoui, and My Abdelouahab Salahddine. "Build a Trained Data of Tesseract OCR engine for Tifinagh Script Recognition." Data and Metadata 2 (December 9, 2023): 185. http://dx.doi.org/10.56294/dm2023185.

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This article introduces a methodology for constructing a trained dataset to facilitate Tifinagh script recognition using the Tesseract OCR engine. The Tifinagh script, widely used in North Africa, poses a challenge due to the lack of built-in recognition capabilities in Tesseract. To overcome this limitation, our approach focuses on image generation, box generation, manual editing, charset extraction, and dataset compilation. By leveraging Python scripting, specialized software tools, and Tesseract's training utilities, we systematically create a comprehensive dataset for Tifinagh script recog
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Mubeen, Dr Suraya, Jally Brahmani, Datha Pavan Kalyan, Ayesha Jagirdar, and A. Praveen Kumar. "Optical Character Recognition Using Tesseract." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 672–75. http://dx.doi.org/10.22214/ijraset.2022.47414.

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Abstract: Optical Character Recognition (OCR) is a process or technology in which text within a digital image is recognized. With rapid pace of technology, people want quicker, handy and reliable tools, which can fulfil their daily needs. With this moto we had gone forward and analyzed the existing tools and made up this Android App, which provides seamless experience (No ads and easy-to-use), and great accuracy. The main objective of this project is to allow automatic extraction of the information that a user wants from the paper document and using it wherever it is needed. In this project, O
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Akhsa, Alvian Tri Putra Darti, Muhammad Agus, Rosmiati Rosmiati, and Andi Muhammad Bahrul Ulum. "Perancangan E-Office Pelayanan Dan Pengarsipan Digital Menggunakan Metode OCR Berbasis Web." INTECOMS: Journal of Information Technology and Computer Science 7, no. 1 (2024): 218–26. http://dx.doi.org/10.31539/intecoms.v7i1.8367.

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Penelitian ini bertujuan untuk memberikan dukungan kepada pemerintah dan masyarakat dalam mengoptimalkan pelayanan dan pengarsipan dokumen sesuai dengan tujuan E-Government. Fokus utamanya adalah meningkatkan efisiensi dan pengorganisasian dalam proses pelayanan publik dan pengarsipan dokumen melalui pemanfaatan metode Optical Character Recognition (OCR). Metode otomatisasi pengelolaan arsip yang diimplementasikan dalam platform ini adalah OCR, yang memiliki peran penting dalam mengubah gambar dokumen menjadi teks yang dapat diolah. Kami menggunakan library tesseract sebagai basis data karakte
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Darpito, Muhammad Noko, Kartika Firdausy, and Abdul Fadlil. "Perbandingan Unjuk Kerja Library Optical Character Recognition (OCR) dalam Pengenalan Teks pada Dokumen Digital." Jurnal Informatika Polinema 11, no. 3 (2025): 273–82. https://doi.org/10.33795/jip.v11i3.7025.

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Optical Character Recognition (OCR) merupakan teknologi yang digunakan untuk mengubah teks dalam dokumen digital menjadi teks yang dapat dikenali oleh mesin. Pemilihan metode OCR yang tepat sangat bergantung pada efisiensi pemrosesan dan akurasi pengenalan teks, terutama dalam penerapan yang membutuhkan kecepatan tinggi dan tingkat kesalahan minimal. Dalam penelitian ini, dilakukan perbandingan performa antara Tesseract dan EasyOCR melalui metode penelitian yang mencakup tahapan pengumpulan data, ekstraksi teks, implementasi OCR menggunakan kedua library tersebut, dan evaluasi hasil ekstraksi
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Dissertations / Theses on the topic "Tesseract OCR"

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Nilsson, Elin. "Test av OCR-verktyg för Linux." Thesis, Linnaeus University, School of Computer Science, Physics and Mathematics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-5906.

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<p>Denna rapport handlar om att ta fram ett OCR-verktyg för digitalisering av pappersdokument. Krav på detta verktyg är att bland annat det ska vara kompatibelt med Linux, det ska kunna ta kommandon via kommandoprompt och dessutom ska det kunna hantera skandinaviska tecken.</p><p>Tolv OCR-verktyg granskades, sedan valdes tre verktyg ut; Ocrad, Tesseract och OCR Shop XTR. För att testa dessa scannades två dokument in och digitaliserades i varje verktyg.</p><p>Resultatet av testerna är att Tesseract är de verktyget som är mest precist och Ocrad är det verktyget som är snabbast. OCR Shop XTR visa
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Larsson, Andreas, and Tony Segerås. "Automated invoice handling with machine learning and OCR." Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188202.

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Companies often process invoices manually, therefore automation could reduce manual labor. The aim of this thesis is to evaluate which OCR-engine, Tesseract or OCRopus, performs best at interpreting invoices. This thesis also evaluates if it is possible to use machine learning to automatically process invoices based on previously stored data. By interpreting invoices with the OCR-engines, it results in the output text having few spelling errors. However, the invoice structure is lost, making it impossible to interpret the corresponding fields. If Naïve Bayes is chosen as the algorithm for mach
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Nell, Henrik. "Quantifying the noise tolerance of the OCR engine Tesseract using a simulated environment." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4028.

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-&gt;Context. Optical Character Recognition (OCR), having a computer recognize text from an image, is not as intuitive as human recognition. Even small (to human eyes) degradations can thwart the OCR result. The problem is that random unknown degradations are unavoidable in a real-world setting. -&gt;Objectives. The noise tolerance of Tesseract, a state-of-the-art OCR engine, is evaluated in relation to how well it handles salt and pepper noise, a type of image degradation. Noise tolerance is measured as the percentage of aberrant pixels when comparing two images (one with noise and the other
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Sahiti, Ylli. "OCR algoritmers noggrannhet och snabbhet vid identifieringen av text på olika typer av bakgrund : En jämförelse mellan OCR - algoritmerna Tesseract och Google ML-Kit." Thesis, Jönköping University, JTH, Avdelningen för datateknik och informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-53789.

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SyfteOCR, optical character recognition, algoritmer kan implementeras på olika sätt, de påverkar även resultatet både beroende på vilken implementation som används och vilket dataset som det används på. Därför är det viktigt att testa de olika OCR algoritmerna på just det dataset som är tänkt att användas för att få ett förutsägbart resultat. Metod60 bilder är tagna på innehållsförteckningar tryckta på svenska livsmedelsprodukter med tre olika bakgrundsytor, aluminium, konvexa ytor och mjukplast. Två OCR algoritmer, ML Kit och Tesseract, har jämförts med avseende på precision och hastighet i s
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Skoglund, Jesper, and Lukas Vikström. "Automating the process of dividing a map image into sections : Using Tesseract OCR and pixel traversing." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148319.

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This paper presents an algorithm with the purpose of automatically dividing a simple floor plan into sections. Sections include names, size and location on the image, all of which will be automatically extracted by the algorithm as a step of converting a simple image into an interactive map. The labels for each section utilizes tesseract-OCR wrapper tesseractJS to extract text and label location. In regards to section borders pixel traversing is employed coupled with CIE76 for color comparison which results in the discovery of size and location of the section. Performance of the algorithm was
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Yasser, Almodhi. "Classifying Receipts and Invoices in Visma Mobile Scanner." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-49671.

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This paper presents a study on classifying receipts and invoices using Machine Learning. Furthermore, Naïve Bayes Algorithm and the advantages of using it will be discussed.  With information gathered from theory and previous research, I will show how to classify images into a receipt or an invoice. Also, it includes pre-processing images using a variety of pre-processing methods and text extraction using Optical Character Recognition (OCR). Moreover, the necessity of pre-processing images to reach a higher accuracy will be discussed. A result shows a comparison between Tesseract OCR engine an
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Arlotti, Elena. "IDCardOCR: Studio di tecniche di OCR per acquisire dati anagrafici mediante scanning di documenti di identità." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7794/.

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Il progetto IDCardOCR si propone di investigare e realizzare le tecnologie per la messa in opera di un servizio avanzato di scanning di documenti di identità e acquisizione automatica dei dati anagrafici in formato strutturato tramite dispositivi mobili. In particolare si vuole realizzare una App Android in grado di: • Acquisire immagini di documenti di identità in diversi formati e rilevare tramite OCR i dati anagrafici. I dati dovranno poi essere salvati in formato strutturato. • Permettere la definizione di diversi template per l’acquisizione di documenti di tipo diverso (patenti, pass
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Bugnerová, Pavla. "Návrh algoritmu pro anonymizaci ultrazvukových dat na úrovni snímku." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316846.

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This master’s thesis is focused on anonymization of ultrasound data in DICOM format. Haar wavelet belonging to Daubechies wavelet family is used to detect text areas in the image. Extraction of the text from the image is done using a free tool - tesseract OCR Engine. Finally, detected text is compared to sensitive data from DICOM metadata using Levenshtein - edit distance algorithm.
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Peřinová, Barbora. "Rozpoznání textu s využitím neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-378026.

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This master’s thesis deals with optical character recognition. The first part describes the basic types of optical character recognition tasks and divides algorithm into individual phases. For each phase the most commonly used methods are described in the next part. Within the character recognition phase the problematics of artificial neural networks and their usage in given phase is explained, specifically multilayer perceptron and convolutional neural networks. The second part deals with requirements definition for specific application to be used as feedback for robotic system. Convolution n
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Maddison, John. "Automatisk validering av skärmgrafik med OpenCV och Tesseract." Thesis, Linköpings universitet, Programvara och system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151912.

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I dagens flygplan finns det mycket information som på ett snabbt och pålitligt sätt behöver förmedlas till piloten via instrument på flera skärmar i cockpit. Att verifiera att skärmarna visar korrekt data för olika indata är ett tidskrävande och monotont arbete. Därför undersöker Saab möjligheten att automatisera delar av arbetet. Examensarbetet undersöker genom praktiskt implementation ifall det är möjligt att automatisera bildanalysen med hjälp av programmen OpenCV och Tesseract. Resultatet visade att det går att enkelt konstruera tester för att automatiskt identifiera oönskade förändringar
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Book chapters on the topic "Tesseract OCR"

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Ramteke, Rakesh, and Mohammed Rashed Ali Omar Al Maamari. "Tesseract OCR Recognition Based on Arabic Machine-Printed Document." In Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022). Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-196-8_27.

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Kathpalia, Nishant, Gabriel Nixon Raj, and Madhavan Venkatesh. "A Smart Healthcare Companion with Tesseract OCR and KNN Integration." In Technologies for Energy, Agriculture, and Healthcare. CRC Press, 2024. https://doi.org/10.1201/9781003596707-30.

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Chopra, Karan, and S. Shanthi Therese. "Gesture-Based Alphabet Detection and Scoring Using OpenCV and Tesseract-OCR." In ICT: Cyber Security and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0744-7_9.

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Joshi, Kartik, and Harshal Arolkar. "Working of the Tesseract OCR on Different Fonts of Gujarati Language." In ICT: Cyber Security and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0744-7_15.

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Tafti, Ahmad P., Ahmadreza Baghaie, Mehdi Assefi, Hamid R. Arabnia, Zeyun Yu, and Peggy Peissig. "OCR as a Service: An Experimental Evaluation of Google Docs OCR, Tesseract, ABBYY FineReader, and Transym." In Advances in Visual Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50835-1_66.

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Joshi, Kartik, and Harshal Arolkar. "A Review of Usage of Tesseract OCR Engine with Vernacular Indian Languages." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-77081-4_1.

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Joshi, Kartik, and Harshal Arolkar. "Comparison of Existing Versus New Model of Tesseract OCR for the Gujarati Language." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-5791-6_27.

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Nirmala, J. S., Rahul Banerjee, and Rajath S. Bharadwaj. "Automatic Vehicular Number Plate Recognition (VNPR) for Identification of Vehicle Using OCR and Tesseract." In Micro-Electronics and Telecommunication Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2329-8_41.

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Chakraborty, Partha, Md Rakib Mia, Humayun Kabir Sumon, et al. "Recognize Meaningful Words and Idioms from the Images Based on OCR Tesseract Engine and NLTK." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1520-8_23.

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Vijaya Krishna, A., and Shaik Naseera. "Vehicle Detection and Categorization for a Toll Charging System Based on TESSERACT OCR Using the IoT." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0212-1_20.

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Conference papers on the topic "Tesseract OCR"

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Krishna, Gopal, Vineeta Singh, Rajkamal Upadhyaya, Harishchander Anandaram, Dilipkumar Jang Bahadur Saini, and Alok Kumar. "Boosting Image-Text Detection Performance with Python Tesseract and the Tesseract OCR Engine." In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit). IEEE, 2024. https://doi.org/10.1109/globalaisummit62156.2024.10947909.

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Samir, Omar, Yousef Waleed, Ibrahim Ahmed, et al. "Fine-Tuning an Arabic OCR Model using Tesseract 5.0." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10928060.

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Gnanakumar, D., S. V. Jairus Ponrabi, N. Ravindran, Ahmed Razvi, and K. Gokulakannan. "Advanced Traffic Violation Detection with Tesseract OCR and Computer Vision." In 2024 International Conference on Circuit, Systems and Communication (ICCSC). IEEE, 2024. http://dx.doi.org/10.1109/iccsc62074.2024.10616606.

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"The Efficacy of Tesseract OCR: Insights from a Practical Application Study." In International Conference on Cutting-Edge Developments in Engineering Technology and Science. ICCDETS, 2024. http://dx.doi.org/10.62919/hdsg3874.

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— This study evaluates the efficacy of the Tesseract Optical Character Recognition (OCR) system through a practical application lens. Tesseract, an open-source OCR tool, is widely recognized for its adaptability and broad usage across various digital imaging and text recognition domains. This paper explores Tesseract's performance in converting scanned documents into editable text formats, emphasizing its accuracy, efficiency, and usability in diverse scenarios, including complex document layouts and varied text quality. By conducting systematic tests across multiple data sets, including print
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Kaur, Jaspreet, Vishal Goyal, and Manish Kumar. "Tesseract OCR for Hindi Typewritten Documents." In 2021 Sixth International Conference on Image Information Processing (ICIIP). IEEE, 2021. http://dx.doi.org/10.1109/iciip53038.2021.9702659.

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Smith, Ray, Daria Antonova, and Dar-Shyang Lee. "Adapting the Tesseract open source OCR engine for multilingual OCR." In the International Workshop. ACM Press, 2009. http://dx.doi.org/10.1145/1577802.1577804.

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Smith, R. "An Overview of the Tesseract OCR Engine." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2. IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4376991.

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Sun, Peiyu, Qiuyan Xie, Zhaokang Wu, Xiaoyu Feng, Jiajun Cai, and Yulian Jiang. "Yi Characters Recognition Based on Tesseract-OCR." In 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2019. http://dx.doi.org/10.1109/imcec46724.2019.8983913.

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Paglinawan, Charmaine C., Marielle Hannah M. Caliolio, and Joshua B. Frias. "Medicine Classification Using YOLOv4 and Tesseract OCR." In 2023 15th International Conference on Computer and Automation Engineering (ICCAE). IEEE, 2023. http://dx.doi.org/10.1109/iccae56788.2023.10111387.

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Thakare, Sahil, Ajay Kamble, Vishal Thengne, and U. R. Kamble. "Document Segmentation and Language Translation Using Tesseract-OCR." In 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2018. http://dx.doi.org/10.1109/iciinfs.2018.8721372.

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