Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues

Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance

The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique

The arrival of handwriting recognition technologies offers new possibilities for research in heritage studies. However, it is now necessary to reflect on the experiences and the practices developed by research teams. Our use of the Transkribus platform since 2018 has led us to search for the most significant ways to improve the performance of our handwritten text recognition (HTR) models which are made to transcribe French handwriting dating from the 17th century. This article therefore reports on the impacts of creating transcribing protocols, using the language model at full scale and determining the best way to use base models in order to help increase the performance of HTR models. Combining all of these elements can indeed increase the performance of a single model by more than 20% (reaching a Character Error Rate below 5%). This article also discusses some challenges regarding the collaborative nature of HTR platforms such as Transkribus and the way researchers can share their data generated in the process of creating or training handwritten text recognition models

Melhorando a precisão do reconhecimento de texto usando técnicas baseadas em sintaxe

Orientadores: Guido Costa Souza de Araújo, Marcio Machado PereiraDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Devido à grande quantidade de informações visuais disponíveis atualmente, a detecção e o reconhecimento de texto em imagens de cenas naturais começaram a ganhar importância nos últimos tempos. Seu objetivo é localizar regiões da imagem onde há texto e reconhecê-lo. Essas tarefas geralmente são divididas em duas partes: detecção de texto e reconhecimento de texto. Embora as técnicas para resolver esse problema tenham melhorado nos últimos anos, o uso excessivo de recursos de hardware e seus altos custos computacionais impactaram significativamente a execução de tais tarefas em sistemas integrados altamente restritos (por exemplo, celulares e TVs inteligentes). Embora existam métodos de detecção e reconhecimento de texto executados em tais sistemas, eles não apresentam bom desempenho quando comparados à soluções de ponta em outras plataformas de computação. Embora atualmente existam vários métodos de pós-correção que melhoram os resultados em documentos históricos digitalizados, há poucas explorações sobre o seu uso nos resultados de imagens de cenas naturais. Neste trabalho, exploramos um conjunto de métodos de pós-correção, bem como propusemos novas heuríticas para melhorar os resultados em imagens de cenas naturais, tendo como base de prototipação o software de reconhecimento de textos Tesseract. Realizamos uma análise com os principais métodos disponíveis na literatura para correção dos erros e encontramos a melhor combinação que incluiu os métodos de substituição, eliminação nos últimos caracteres e composição. Somado a isto, os resultados mostraram uma melhora quando introduzimos uma nova heurística baseada na frequência com que os possíveis resultados aparecem em bases de dados de magazines, jornais, textos de ficção, web, etc. Para localizar erros e evitar overcorrection foram consideradas diferentes restrições obtidas através do treinamento da base de dados do Tesseract. Selecionamos como melhor restrição a incerteza do melhor resultado obtido pelo Tesseract. Os experimentos foram realizados com sete banco de dados usados em sites de competição na área, considerando tanto banco de dados para desafio em reconhecimento de texto e aqueles com o desafio de detecção e reconhecimento de texto. Em todos os bancos de dados, tanto nos dados de treinamento como de testes, os resultados do Tesseract com o método proposto de pós-correção melhorou consideravelmente em comparação com os resultados obtidos somente com o TesseractAbstract: Due to a large amount of visual information available today, Text Detection and Recognition in scene images have begun to receive an increasing importance. The goal of this task is to locate regions of the image where there is text and recognize them. Such tasks are typically divided into two parts: Text Detection and Text Recognition. Although the techniques to solve this problem have improved in recent years, the excessive usage of hardware resources and its corresponding high computational costs have considerably impacted the execution of such tasks in highly constrained embedded systems (e.g., cellphones and smart TVs). Although there are Text Detection and Recognition methods that run in such systems they do not have good performance when compared to state-of-the-art solutions in other computing platforms. Although there are currently various post-correction methods to improve the results of scanned documents, there is a little effort in applying them on scene images. In this work, we explored a set of post-correction methods, as well as proposed new heuristics to improve the results in scene images, using the Tesseract text recognition software as a prototyping base. We performed an analysis with the main methods available in the literature to correct errors and found the best combination that included the methods of substitution, elimination in the last characters, and compounder. In addition, results showed an improvement when we introduced a new heuristic based on the frequency with which the possible results appear in the frequency databases for categories such as magazines, newspapers, fiction texts, web, etc. In order to locate errors and avoid overcorrection, different restrictions were considered through Tesseract with the training database. We selected as the best restriction the certainty of the best result obtained by Tesseract. The experiments were carried out with seven databases used in Text Recognition and Text Detection/Recognition competitions. In all databases, for both training and testing, the results of Tesseract with the proposed post-correction method considerably improved when compared to the results obtained only with TesseractMestradoCiência da ComputaçãoMestra em Ciência da Computação4716-1488887.335287/2019-00, 1774549FuncampCAPE

Text-detection and -recognition from natural images

Text detection and recognition from images could have numerous functional applications for document analysis, such as assistance for visually impaired people; recognition of vehicle license plates; evaluation of articles containing tables, street signs, maps, and diagrams; keyword-based image exploration; document retrieval; recognition of parts within industrial automation; content-based extraction; object recognition; address block location; and text-based video indexing. This research exploited the advantages of artificial intelligence (AI) to detect and recognise text from natural images. Machine learning and deep learning were used to accomplish this task.In this research, we conducted an in-depth literature review on the current detection and recognition methods used by researchers to identify the existing challenges, wherein the differences in text resulting from disparity in alignment, style, size, and orientation combined with low image contrast and a complex background make automatic text extraction a considerably challenging and problematic task. Therefore, the state-of-the-art suggested approaches obtain low detection rates (often less than 80%) and recognition rates (often less than 60%). This has led to the development of new approaches. The aim of the study was to develop a robust text detection and recognition method from natural images with high accuracy and recall, which would be used as the target of the experiments. This method could detect all the text in the scene images, despite certain specific features associated with the text pattern. Furthermore, we aimed to find a solution to the two main problems concerning arbitrarily shaped text (horizontal, multi-oriented, and curved text) detection and recognition in a low-resolution scene and with various scales and of different sizes.In this research, we propose a methodology to handle the problem of text detection by using novel combination and selection features to deal with the classification algorithms of the text/non-text regions. The text-region candidates were extracted from the grey-scale images by using the MSER technique. A machine learning-based method was then applied to refine and validate the initial detection. The effectiveness of the features based on the aspect ratio, GLCM, LBP, and HOG descriptors was investigated. The text-region classifiers of MLP, SVM, and RF were trained using selections of these features and their combinations. The publicly available datasets ICDAR 2003 and ICDAR 2011 were used to evaluate the proposed method. This method achieved the state-of-the-art performance by using machine learning methodologies on both databases, and the improvements were significant in terms of Precision, Recall, and F-measure. The F-measure for ICDAR 2003 and ICDAR 2011 was 81% and 84%, respectively. The results showed that the use of a suitable feature combination and selection approach could significantly increase the accuracy of the algorithms.A new dataset has been proposed to fill the gap of character-level annotation and the availability of text in different orientations and of curved text. The proposed dataset was created particularly for deep learning methods which require a massive completed and varying range of training data. The proposed dataset includes 2,100 images annotated at the character and word levels to obtain 38,500 samples of English characters and 12,500 words. Furthermore, an augmentation tool has been proposed to support the proposed dataset. The missing of object detection augmentation tool encroach to proposed tool which has the ability to update the position of bounding boxes after applying transformations on images. This technique helps to increase the number of samples in the dataset and reduce the time of annotations where no annotation is required. The final part of the thesis presents a novel approach for text spotting, which is a new framework for an end-to-end character detection and recognition system designed using an improved SSD convolutional neural network, wherein layers are added to the SSD networks and the aspect ratio of the characters is considered because it is different from that of the other objects. Compared with the other methods considered, the proposed method could detect and recognise characters by training the end-to-end model completely. The performance of the proposed method was better on the proposed dataset; it was 90.34. Furthermore, the F-measure of the method’s accuracy on ICDAR 2015, ICDAR 2013, and SVT was 84.5, 91.9, and 54.8, respectively. On ICDAR13, the method achieved the second-best accuracy. The proposed method could spot text in arbitrarily shaped (horizontal, oriented, and curved) scene text.</div

Limits on the application of frequency-based language models to OCR

Abstract—Although large language models are used in speech recognition and machine translation applications, OCR systems are “far behind ” in their use of language models. The reason for this is not the laggardness of the OCR community, but the fact that, at high accuracies, a frequency-based language model can do more damage than good, unless carefully applied. This paper presents an analysis of this discrepancy with the help of the Google Books n-gram Corpus, and concludes that noisy-channel models that closely model the underlying classifier and segmentation errors are required. Keywords-OCR; language models. I

Qualität in der Inhaltserschließung

This edited volume deals with issues relating to the quality of subject cataloging in the digital age, where heterogenous articles from different processes meet, and attempts to define important quality standards. Topics range from metadata and the cataloging policies of the German National Library, the GND, and the head offices of the German library association, to the presentation of a range of different projects, such as QURATOR and SoNAR