Relevant bibliographies by topics / OCR,Computer Vision

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'OCR,Computer Vision'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "OCR,Computer Vision"

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Lázaro, Jesús, José Luis Martín, Jagoba Arias, Armando Astarloa, and Carlos Cuadrado. "Neuro semantic thresholding using OCR software for high precision OCR applications." Image and Vision Computing 28, no. 4 (April 2010): 571–78. http://dx.doi.org/10.1016/j.imavis.2009.09.011.

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Draghici, Sorin. "A Neural Network Based Artificial Vision System for Licence Plate Recognition." International Journal of Neural Systems 08, no. 01 (February 1997): 113–26. http://dx.doi.org/10.1142/s0129065797000148.

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This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solution used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%
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Cahyadi, Septian, Febri Damatraseta, and Lodryck Lodefikus S. "Comparative Analysis Of Efficient Image Segmentation Technique For Text Recognition And Human Skin Recognition." Jurnal Informatika Kesatuan 1, no. 1 (July 13, 2021): 81–90. http://dx.doi.org/10.37641/jikes.v1i1.775.

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Computer Vision and Pattern Recognition is one of the most interesting research subject on computer science, especially in case of reading or recognition of objects in realtime from the camera device. Object detection has wide range of segments, in this study we will try to find where the better methodologies for detecting a text and human skin. This study aims to develop a computer vision technology that will be used to help people with disabilities, especially illiterate (tuna aksara) and deaf (penyandang tuli) to recognize and learn the letters of the alphabet (A-Z). Based on our research, it is found that the best method and technique used for text recognition is Convolutional Neural Network with achievement accuracy reaches 93%, the next best achievement obtained OCR method, which reached 98% on the reading plate number. And also OCR method are 88% with stable image reading and good lighting conditions as well as the standard font type of a book. Meanwhile, best method and technique to detect human skin is by using Skin Color Segmentation: CIELab color space with accuracy of 96.87%. While the algorithm for classification using Convolutional Neural Network (CNN), the accuracy rate of 98% Key word: Computer Vision, Segmentation, Object Recognition, Text Recognition, Skin Color Detection, Motion Detection, Disability Application
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Ong, Veronica, and Derwin Suhartono. "Using K-Nearest Neighbor in Optical Character Recognition." ComTech: Computer, Mathematics and Engineering Applications 7, no. 1 (March 1, 2016): 53. http://dx.doi.org/10.21512/comtech.v7i1.2223.

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The growth in computer vision technology has aided society with various kinds of tasks. One of these tasks is the ability of recognizing text contained in an image, or usually referred to as Optical Character Recognition (OCR). There are many kinds of algorithms that can be implemented into an OCR. The K-Nearest Neighbor is one such algorithm. This research aims to find out the process behind the OCR mechanism by using K-Nearest Neighbor algorithm; one of the most influential machine learning algorithms. It also aims to find out how precise the algorithm is in an OCR program. To do that, a simple OCR program to classify alphabets of capital letters is made to produce and compare real results. The result of this research yielded a maximum of 76.9% accuracy with 200 training samples per alphabet. A set of reasons are also given as to why the program is able to reach said level of accuracy.
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Dozias, Anne, Cristian Camilo Otalora-Leguizamón, Marco Bianchetti, and Maria Susana Avila-Garcia. "Smart pens to assist fibre optic sensors research: Evaluating OCR tools." Avances en Interacción Humano-Computadora, no. 1 (October 31, 2018): 41. http://dx.doi.org/10.47756/aihc.y3i1.42.

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Reproducibility is one of the big challenges in research. Lab notebooks have been used to record data, observations and relevant remarks of the research processes. Smart pens are devices that record audio, handwriting notes thanks to micro patterned paper, and generate pdf files and audio enriched notes (pencasts). The handwriting notes can then be processed using optical character recognition (OCR) software to generate digital documents allowing the user to archive and access these notes in an easier way. However, OCR for handwriting is still a challenge in the computer vision research area. In this paper, we report the evaluation results of different OCR tools when processing handwriting notes written by 7 participants focusing on the main elements and technical vocabulary identified in fibre optic sensors research.
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Drobac, Senka, and Krister Lindén. "Optical character recognition with neural networks and post-correction with finite state methods." International Journal on Document Analysis and Recognition (IJDAR) 23, no. 4 (August 20, 2020): 279–95. http://dx.doi.org/10.1007/s10032-020-00359-9.

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Abstract The optical character recognition (OCR) quality of the historical part of the Finnish newspaper and journal corpus is rather low for reliable search and scientific research on the OCRed data. The estimated character error rate (CER) of the corpus, achieved with commercial software, is between 8 and 13%. There have been earlier attempts to train high-quality OCR models with open-source software, like Ocropy (https://github.com/tmbdev/ocropy) and Tesseract (https://github.com/tesseract-ocr/tesseract), but so far, none of the methods have managed to successfully train a mixed model that recognizes all of the data in the corpus, which would be essential for an efficient re-OCRing of the corpus. The difficulty lies in the fact that the corpus is printed in the two main languages of Finland (Finnish and Swedish) and in two font families (Blackletter and Antiqua). In this paper, we explore the training of a variety of OCR models with deep neural networks (DNN). First, we find an optimal DNN for our data and, with additional training data, successfully train high-quality mixed-language models. Furthermore, we revisit the effect of confidence voting on the OCR results with different model combinations. Finally, we perform post-correction on the new OCR results and perform error analysis. The results show a significant boost in accuracy, resulting in 1.7% CER on the Finnish and 2.7% CER on the Swedish test set. The greatest accomplishment of the study is the successful training of one mixed language model for the entire corpus and finding a voting setup that further improves the results.
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RICE, STEPHEN V., JUNICHI KANAI, and THOMAS A. NARTKER. "AN ALGORITHM FOR MATCHING OCR-GENERATED TEXT STRINGS." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 05 (October 1994): 1259–68. http://dx.doi.org/10.1142/s0218001494000632.

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When optical character recognition (OCR) devices process the same page image, they generate similar text strings. Differences are due to recognition errors. A page of text rarely contains long repeated substrings; therefore, N strings generated by OCR devices can be quickly matched by detecting long common substrings. An algorithm for matching an arbitrary number of strings based on this principle is presented. Although its worst-case performance is O(Nn2), its performance in practice has been observed to be O(Nn log n), where n is the length of a string. This algorithm has been successfully used to study OCR errors, to determine the accuracy of OCR devices, and to implement a voting algorithm.
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Veeramachaneni, Sriharsha, and George Nagy. "Adaptive classifiers for multisource OCR." International Journal on Document Analysis and Recognition 6, no. 3 (March 1, 2003): 154–66. http://dx.doi.org/10.1007/s10032-003-0108-x.

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Fataicha, Y., M. Cheriet, J. Y. Nie, and C. Y. Suen. "Retrieving poorly degraded OCR documents." International Journal of Document Analysis and Recognition (IJDAR) 8, no. 1 (October 13, 2005): 15–26. http://dx.doi.org/10.1007/s10032-005-0147-6.

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Clausner, Christian, Apostolos Antonacopoulos, and Stefan Pletschacher. "Efficient and effective OCR engine training." International Journal on Document Analysis and Recognition (IJDAR) 23, no. 1 (October 30, 2019): 73–88. http://dx.doi.org/10.1007/s10032-019-00347-8.

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Abstract We present an efficient and effective approach to train OCR engines using the Aletheia document analysis system. All components required for training are seamlessly integrated into Aletheia: training data preparation, the OCR engine’s training processes themselves, text recognition, and quantitative evaluation of the trained engine. Such a comprehensive training and evaluation system, guided through a GUI, allows for iterative incremental training to achieve best results. The widely used Tesseract OCR engine is used as a case study to demonstrate the efficiency and effectiveness of the proposed approach. Experimental results are presented validating the training approach with two different historical datasets, representative of recent significant digitisation projects. The impact of different training strategies and training data requirements is presented in detail.
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Dissertations / Theses on the topic "OCR,Computer Vision"

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Poli, Flavio. "Robust string text detection for industrial OCR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/12885/.

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Tesi che propone un algoritmo per il ritrovamento di linee di testo per OCR industriali. Tramite un aproccio ad albero e sfruttando la conoscenza sulla stringa da cercare, vengono esplorate più soluzioni fino a trovare quella più promettente. Fornisce in uscita anche una stima su quanto l'algoritmo è confidente sul risultato.
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Belgiovine, Mauro. "Advanced industrial OCR using Autoencoders." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13807/.

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Il contenuto di questa tesi di laurea descrive il lavoro svolto durante un tirocinio di sei mesi presso Datalogic ADC. L'obiettivo del lavoro è stato quello di utilizzare uno specifico tipo di rete neurale, chiamata Autoencoder, per scopi legati al riconoscimento o alla convalida di caratteri in un sistema OCR industriale. In primo luogo è stato creato un classificatore di immagini di caratteri basato su Denoising Autoencoder; successivamente, è stato studiato un metodo per utilizzare l'Autoencoder come un classificatore di secondo livello, per meglio distinguere le false attivazioni da quelle corrette in condizioni di incertezza di un classificatore generico. Entrambe le architetture sono state valutate su dataset reali di clienti di Datalogic e i risultati sperimentali ottenuti sono presentati in questa tesi.
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Serafini, Sara. "Machine Learning applied to OCR tasks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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The content of this thesis describes the work done during a six-month internship at Datalogic, in its research laboratories in Pasadena (CA). The aim of my research was to implement and evaluate a classifier as part of an industrial OCR system for learning purposes and to see how well it could work in comparison to current best Datalogic products, since it might be simpler/faster, it might be a good alternative for implementing on an embedded system (where current Datalogic products may not be able to run fast enough).
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Corsi, Giacomo. "Fast Neural Network Technique for Industrial OCR." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15258/.

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The content of my thesis describes the work done during my internship at Datalogic in Pasadena. This project improves the performance of the Optical Character Recognition (OCR) solution with use of Deep Learning (DL) techniques. It enhances the character detection process that had been previously developed and relies on template matching done on the Histogram of Gradients (HOG) features. This approach had been already validated with good performance, but detects only those characters which do not vary in the dataset. First, this document gives a introduction to OCR and DL topics, then describes the pipeline of the Datalogic OCR product. After that, it is explained the technique that was usedto raise the accuracy of the previous solution. It consists in applying DL to improve the robustness and keep good detection rate even though the character variations (scale and rotation) are considerable. The first phase was focused on speeding up the process and so the function used for gauging the matching with the templates, the Zero-mean Normalized Cross-Correlation, was replaced while a modified version, called Squared Normalization has been introduced. Secondly, the original system was cast as a Convolutional Neural Network (CNN) by turning the HOG templates into convolutional kernels. It was necessary to rethink its training process as it was noticed that, using standard target values, there was no gain. A novel way of computing the targets, named Graceful Improvement, has been developed. Then, the analysis on the results of this new solution showed that, even ifit detects characters that present variations with original templates, the false positive rate around the image was also higher. To decrease this negative side effect, a fast ROI (Region Of Interest) filter acting on the detections has been realized. Finally, during the above development steps, performances in terms of accuracy and time have been evaluated on some real Datalogic's customer datasets.
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Johansson, Elias. "Separation and Extraction of Valuable Information From Digital Receipts Using Google Cloud Vision OCR." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-88602.

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Automatization is a desirable feature in many business areas. Manually extracting information from a physical object such as a receipt is something that can be automated to save resources for a company or a private person. In this paper the process will be described of combining an already existing OCR engine with a developed python script to achieve data extraction of valuable information from a digital image of a receipt. Values such as VAT, VAT%, date, total-, gross-, and net-cost; will be considered as valuable information. This is a feature that has already been implemented in existing applications. However, the company that I have done this project for are interested in creating their own version. This project is an experiment to see if it is possible to implement such an application using restricted resources. To develop a program that can extract the information mentioned above. In this paper you will be guided though the process of the development of the program. As well as indulging in the mindset, findings and the steps taken to overcome the problems encountered along the way. The program achieved a success rate of 86.6% in extracting the most valuable information: total cost, VAT% and date from a set of 53 receipts originated from 34 separate establishments.
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Grönlund, Jakob, and Angelina Johansson. "Defect Detection and OCR on Steel." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157508.

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In large scale productions of metal sheets, it is important to maintain an effective way to continuously inspect the products passing through the production line. The inspection mainly consists of detection of defects and tracking of ID numbers. This thesis investigates the possibilities to create an automatic inspection system by evaluating different machine learning algorithms for defect detection and optical character recognition (OCR) on metal sheet data. Digit recognition and defect detection are solved separately, where the former compares the object detection algorithm Faster R-CNN and the classical machine learning algorithm NCGF, and the latter is based on unsupervised learning using a convolutional autoencoder (CAE). The advantage of the feature extraction method is that it only needs a couple of samples to be able to classify new digits, which is desirable in this case due to the lack of training data. Faster R-CNN, on the other hand, needs much more training data to solve the same problem. NCGF does however fail to classify noisy images and images of metal sheets containing an alloy, while Faster R-CNN seems to be a more promising solution with a final mean average precision of 98.59%. The CAE approach for defect detection showed promising result. The algorithm learned how to only reconstruct images without defects, resulting in reconstruction errors whenever a defect appears. The errors are initially classified using a basic thresholding approach, resulting in a 98.9% accuracy. However, this classifier requires supervised learning, which is why the clustering algorithm Gaussian mixture model (GMM) is investigated as well. The result shows that it should be possible to use GMM, but that it requires a lot of GPU resources to use it in an end-to-end solution with a CAE.
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Dürebrandt, Jesper. "Segmentation and Beautification of Handwriting using Mobile Devices." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-251948.

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Converting handwritten or machine printed documents into a computer readable format allows more efficient storage and processing. The recognition of machine printed text is very reliable with today's technology, but the recognition of offline handwriting still remains a problem to the research community due to the high variance in handwriting styles. Modern mobile devices are capable of performing complex tasks such as scanning invoices, reading traffic signs, and online handwriting recognition, but there are only a few applications that treat offline handwriting. This thesis investigates the segmentation of handwritten documents into text lines and words, how the legibility of handwriting can be increased by beautification, as well as implementing it for modern mobile devices. Text line and word segmentation are crucial steps towards implementing a complete handwriting recognition system. The results of this thesis show that text line and word segmentation along with handwriting beautification can be implemented successfully for modern mobile devices and a survey concluding that the writing on processed documents is more legible than their unprocessed counterparts. An application for the operating system iOS is developed for demonstration.
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Paul, Priya. "Automated test development for vehicle instrument panel cluster using Hardware-in-the-loop (HIL) and Computer Vision." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Automation of validation tests can significantly save time and improve accuracy. This thesis presents a method to automate the tests done on the Instrument Panel Cluster (IPC) of a car by using Hardware-in-the-Loop (HIL) and Optical Character Recognition (OCR). HIL technique helps to test the system with real signals and the system with the camera captures pictures to do OCR analysis for the extraction of messages displayed in the IPC. The developed OCR feature is added to the existing automation tool of the FCA group and the tests conducted in the internal test benches of Maserati. OCR technique is widely used in the automotive sector for validation testing of the IPC. In this thesis, the development is done by first performing a sequence of image processing on the captured image of the IPC and then feeding it to the OCR engine with the required language. The result showed the system to work efficiently as it extracted the messages from the captured images with confidence values close to 90 percent.The testing was done in different languages and low confidence values were found only for some languages with complex letters. After the developed OCR feature was integrated to the internal automation tool, tests were carried out both in the functional test bench and the integration test bench. A test case was defined based on a specific vehicle function and the final pass or fail report generated automatically.
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Zhu, Yuehan. "Automated Supply-Chain Quality Inspection Using Image Analysis and Machine Learning." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-20069.

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An image processing method for automatic quality assurance of Ericsson products is developed. The method consists of taking an image of the product, extract the product labels from the image, OCR the product numbers and make a database lookup to match the mounted product with the customer specification. The engineering innovation of the method developed in this report is that the OCR is performed using machine learning techniques. It is shown that machine learning can produce results that are on par or better than baseline OCR methods. The advantage with a machine learning based approach is that the associated neural network can be trained for the specific input images from the Ericsson factory. Imperfections in the image quality and varying type fonts etc. can be handled by properly training the net, a task that would have been very difficult with legacy OCR algorithms where poor OCR results typically need to be mitigated by improving the input image quality rather than changing the algorithm.
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Lamberti, Lorenzo. "A deep learning solution for industrial OCR applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19777/.

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This thesis describes a project developed throughout a six months internship in the Machine Vision Laboratory of Datalogic based in Pasadena, California. The project aims to develop a deep learning system as a possible solution for industrial optical character recognition applications. In particular, the focus falls on a specific algorithm called You Only Look Once (YOLO), which is a general-purpose object detector based on convolutional neural networks that currently offers state-of-the-art performances in terms of trade-off between speed and accuracy. This algorithm is indeed well known for reaching impressive processing speeds, but its intrinsic structure makes it struggle in detecting small objects clustered together, which unfortunately matches our scenario: we are trying to read alphanumerical codes by detecting each single character and then reconstructing the final string. The final goal of this thesis is to overcome this drawback and push the accuracy performances of a general object detector convolutional neural network to its limits, in order to meet the demanding requirements of industrial OCR applications. To accomplish this, first YOLO's unique detecting approach was mastered in its original framework called Darknet, written in C and CUDA, then all the code was translated into Python programming language for a better flexibility, which also allowed the deployment of a custom architecture. Four different datasets with increasing complexity were used as case-studies and the final performances reached were surprising: the accuracy varies between 99.75\% and 99.97\% with a processing time of 15 ms for images $1000\times1000$ big, largely outperforming in speed the current deep learning solution deployed by Datalogic. On the downsides, the training phase usually requires a very large amount of data and time and YOLO also showed some memorization behaviours if not enough variability is given at training time.
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Books on the topic "OCR,Computer Vision"

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Haikal, El Abed, and SpringerLink (Online service), eds. Guide to OCR for Arabic Scripts. London: Springer London, 2012.

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Wall, Jeff. Jeff Wall: Space and vision. München: Schirmer/Mosel, 1997.

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International Fall Workshop Vision, Modeling, and Visualization (8th 2003 Munich, Germany). Vision, modeling, and visualization 2003: Proceedings, November 19 - 21, 2003, München, Germany. Berlin: AKA, 2003.

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Märgner, Volker, and Haikal El Abed. Guide to OCR for Arabic Scripts. Springer, 2014.

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Märgner, Volker, and Haikal El Abed. Guide to OCR for Arabic Scripts. Springer, 2012.

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Gelernter, David. Mirror Worlds. Oxford University Press, 1991. http://dx.doi.org/10.1093/oso/9780195068122.001.0001.

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Technology doesn't flow smoothly; it's the big surprises that matter, and Yale computer expert David Gelernter sees one such giant leap right on the horizon. Today's small scale software programs are about to be joined by vast public software works that will revolutionize computing and transform society as a whole. One such vast program is the "Mirror world." Imagine looking at your computer screen and seeing reality--an image of your city, for instance, complete with moving traffic patterns, or a picture that sketches the state of an entire far-flung corporation at this second. These representations are called Mirror worlds, and according to Gelernter they will soon be available to everyone. Mirror worlds are high-tech voodoo dolls: by interacting with the images, you interact with reality. Indeed, Mirror worlds will revolutionize the use of computers, transforming them from (mere) handy tools to crystal balls which will allow us to see the world more vividly and see into it more deeply. Reality will be replaced gradually, piece-by-piece, by a software imitation; we will live inside the imitation; and the surprising thing is--this will be a great humanistic advance. we gain control over our world, plus a huge new measure of insight and vision. In this fascinating book--part speculation, part explanation--Gelernter takes us on a tour of the computer technology of the near future. Mirror worlds, he contends, will allow us to explore the world in unprecedented depth and detail without ever changing out of our pajamas. A hospital administrator might wander through an entire medical complex via a desktop computer. Any citizen might explore the performance of the local schools, chat electronically with teachers and other Mirror world visitors, plant software agents to report back on interesting topics; decide to run for the local school board, hire a campaign manager, and conduct the better part of the campaign itself--all by interacting with the Mirror world. Gelernter doesn't just speculate about how this amazing new software will be used--he shows us how it will be made, explaining carefully and in detail how to build a Mirror world using technology already available. we learn about "disembodied machines," "trellises," "ensembles," and other computer components which sound obscure, but which Gelernter explains using familiar metaphors and terms. (He tells us that a Mirror world is a microcosm just like a Japanese garden or a Gothic cathedral, and that a computer program is translated by the computer in the same way a symphony is translated by a violinist into music.) Mirror worlds offers a lucid and humanistic account of the coming software revolution, told by a computer scientist at the cutting edge of his field.
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Wall, Jeff. Jeff Wall: Space and Vision. Schirmer/Mosel, 1996.

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(Editor), Thomas Ertl, B. Girod (Editor), G. Greiner (Editor), H. Niemann (Editor), H. P. Seidel (Editor), E. Steinbach (Editor), and R. Westermann (Editor), eds. Vision, Modeling, and Visualization 2003: Proceedings November 19-21, 2003, Munchen, Germany. O C S L Press, 2003.

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Wang, Jason T. L., Bruce A. Shapiro, and Dennis Shasha, eds. Pattern Discovery in Biomolecular Data. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195119404.001.0001.

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Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.
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Sun, Changming, Hugues Talbot, Sebastien Ourselin, and Tony Adriaansen, eds. Digital Image Computing: Techniques and Applications. CSIRO Publishing, 2003. http://dx.doi.org/10.1071/9780643090989.

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Digital Image Computing: Techniques and Applications is the premier biennial conference in Australia on the topics of image processing and image analysis. This seventh edition of the proceedings has seen an unprecedented level of submission, on such diverse areas as: Image processing; Face recognition; Segmentation; Registration; Motion analysis; Medical imaging; Object recognition; Virtual environments; Graphics; Stereo-vision; and Video analysis. These two volumes contain all the 108 accepted papers and five invited talks that were presented at the conference. These two volumes provide the Australian and international imaging research community with a snapshot of current theoretical and practical developments in these areas. They are of value to any engineer, computer scientist, mathematician, statistician or student interested in these matters.
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Book chapters on the topic "OCR,Computer Vision"

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Ranjan, Ashish, Varun Nagesh Jolly Behera, and Motahar Reza. "OCR Using Computer Vision and Machine Learning." In Studies in Computational Intelligence, 83–105. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50641-4_6.

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Soundararajan, Padmanabhan, Matthew Boonstra, Vasant Manohar, Valentina Korzhova, Dmitry Goldgof, Rangachar Kasturi, Shubha Prasad, Harish Raju, Rachel Bowers, and John Garofolo. "Evaluation Framework for Video OCR." In Computer Vision, Graphics and Image Processing, 829–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_74.

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Lin, Xiaofan, Xiaoqing Ding, Youbin Chen, Jinhui Liu, and Youshou Wu. "Evaluation and application of recognition confidence in OCR." In Computer Vision — ACCV'98, 160–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63930-6_117.

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Javed, Sobia Tariq, and Sarmad Hussain. "Segmentation Based Urdu Nastalique OCR." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 41–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41827-3_6.

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Vasantha Lakshmi, C., Ritu Jain, and C. Patvardhan. "OCR of Printed Telugu Text with High Recognition Accuracies." In Computer Vision, Graphics and Image Processing, 786–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_70.

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Li, Liangcheng, Feiyu Gao, Jiajun Bu, Yongpan Wang, Zhi Yu, and Qi Zheng. "An End-to-End OCR Text Re-organization Sequence Learning for Rich-Text Detail Image Comprehension." In Computer Vision – ECCV 2020, 85–100. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58595-2_6.

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Pekala, M., N. Joshi, T. Y. Alvin Liu, N. M. Bressler, D. Cabrera DeBuc, and P. Burlina. "OCT Segmentation via Deep Learning: A Review of Recent Work." In Computer Vision – ACCV 2018 Workshops, 316–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21074-8_27.

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Li, Yuchun, Sijie Niu, Zexuan Ji, and Qiang Chen. "Automated and Robust Geographic Atrophy Segmentation for Time Series SD-OCT Images." In Pattern Recognition and Computer Vision, 249–61. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03398-9_22.

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Alonso-Caneiro, David, Jason Kugelman, Jared Hamwood, Scott A. Read, Stephen J. Vincent, Fred K. Chen, and Michael J. Collins. "Automatic Retinal and Choroidal Boundary Segmentation in OCT Images Using Patch-Based Supervised Machine Learning Methods." In Computer Vision – ACCV 2018 Workshops, 215–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21074-8_17.

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Degoun, Mohamad, A. Drissi, Pierre Fenies, Vincent Giard, K. Retmi, and J. Saadi. "General Use of the Routing Concept for Supply Chain Modeling Purposes: The Case of OCP S.A." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 323–33. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44739-0_40.

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Conference papers on the topic "OCR,Computer Vision"

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Hassibi, Khosrow M. "Machine-printed Arabic OCR." In Interdisciplinary Computer Vision: Applications and Changing Needs--22nd AIPR Workshop, edited by J. Michael Selander. SPIE, 1994. http://dx.doi.org/10.1117/12.169463.

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Mor, Noam, and Lior Wolf. "Confidence Prediction for Lexicon-Free OCR." In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. http://dx.doi.org/10.1109/wacv.2018.00030.

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Fataicha, Y., M. Cheriet, J. Y. Nie, and C. Y. Suen. "Information Retrieval Based on OCR Errors in Scanned Documents." In 2003 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). IEEE, 2003. http://dx.doi.org/10.1109/cvprw.2003.10020.

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Mulgaonkar, Prasanna G., Chien-Huei Chen, and Jeff L. DeCurtins. "Word recognition in a segmentation-free approach to OCR." In Interdisciplinary Computer Vision: Applications and Changing Needs--22nd AIPR Workshop, edited by J. Michael Selander. SPIE, 1994. http://dx.doi.org/10.1117/12.169464.

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Aharrane, Nabil, Karim El Moutaouakil, and Khalid Satori. "A comparison of supervised classification methods for a statistical set of features: Application: Amazigh OCR." In 2015 Intelligent Systems and Computer Vision (ISCV). IEEE, 2015. http://dx.doi.org/10.1109/isacv.2015.7106171.

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Lee, Chen-Yu, and Simon Osindero. "Recursive Recurrent Nets with Attention Modeling for OCR in the Wild." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.245.

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Ghosh, Subhankar, Ujjwol Barman, P. K. Bora, Tourangbam Harishore Singh, and B. B. Chaudhuri. "An OCR system for the Meetei Mayek script." In 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE, 2013. http://dx.doi.org/10.1109/ncvpripg.2013.6776228.

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Dwivedi, Agam, Rohit Saluja, and Ravi Kiran Sarvadevabhatla. "An OCR for Classical Indic Documents Containing Arbitrarily Long Words." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00288.

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Kae, Andrew, Gary Huang, Carl Doersch, and Erik Learned-Miller. "Improving state-of-the-art OCR through high-precision document-specific modeling." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5539867.

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Strohmaier, Christian, Christoph Ringlstetter, Klaus U. Schulz, and Stoyan Mihov. "A visual and interactive tool for optimizing lexical postcorrection of OCR results." In 2003 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). IEEE, 2003. http://dx.doi.org/10.1109/cvprw.2003.10031.

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Reports on the topic "OCR,Computer Vision"

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Stephenson, Scott A. U.S. Space Command's Role in Computer Network Defense: 2020 Vision or Hack Job? Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada405816.

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