Text Detection Forgot About Document OCR

Detection and recognition of text from scans and other images, commonly denoted as Optical Character Recognition (OCR), is a widely used form of automated document processing with a number of methods available. Yet OCR systems still do not achieve 100% accuracy, requiring human corrections in applications where correct readout is essential. Advances in machine learning enabled even more challenging scenarios of text detection and recognition "in-the-wild" - such as detecting text on objects from photographs of complex scenes. While the state-of-the-art methods for in-the-wild text recognition are typically evaluated on complex scenes, their performance in the domain of documents is typically not published, and a comprehensive comparison with methods for document OCR is missing. This paper compares several methods designed for in-the-wild text recognition and for document text recognition, and provides their evaluation on the domain of structured documents. The results suggest that state-of-the-art methods originally proposed for in-the-wild text detection also achieve competitive results on document text detection, outperforming available OCR methods. We argue that the application of document OCR should not be omitted in evaluation of text detection and recognition methods.Comment: Accepted to the 26th Computer Vision Winter Workshop (CVWW), 202

Plant recognition by AI: Deep neural nets, transformers, and kNN in deep embeddings

The article reviews and benchmarks machine learning methods for automatic image-based plant species recognition and proposes a novel retrieval-based method for recognition by nearest neighbor classification in a deep embedding space. The image retrieval method relies on a model trained via the Recall@k surrogate loss. State-of-the-art approaches to image classification, based on Convolutional Neural Networks (CNN) and Vision Transformers (ViT), are benchmarked and compared with the proposed image retrieval-based method. The impact of performance-enhancing techniques, e.g., class prior adaptation, image augmentations, learning rate scheduling, and loss functions, is studied. The evaluation is carried out on the PlantCLEF 2017, the ExpertLifeCLEF 2018, and the iNaturalist 2018 Datasets-the largest publicly available datasets for plant recognition. The evaluation of CNN and ViT classifiers shows a gradual improvement in classification accuracy. The current state-of-the-art Vision Transformer model, ViT-Large/16, achieves 91.15% and 83.54% accuracy on the PlantCLEF 2017 and ExpertLifeCLEF 2018 test sets, respectively; the best CNN model (ResNeSt-269e) error rate dropped by 22.91% and 28.34%. Apart from that, additional tricks increased the performance for the ViT-Base/32 by 3.72% on ExpertLifeCLEF 2018 and by 4.67% on PlantCLEF 2017. The retrieval approach achieved superior performance in all measured scenarios with accuracy margins of 0.28%, 4.13%, and 10.25% on ExpertLifeCLEF 2018, PlantCLEF 2017, and iNat2018-Plantae, respectively

A deep learning method for visual recognition of snake species

The paper presents a method for image-based snake species identification. The proposed method is based on deep residual neural networks - ResNeSt, ResNeXt and ResNet - fine-tuned from ImageNet pre-trained checkpoints. We achieve performance improvements by: discarding predictions of species that do not occur in the country of the query; combining predictions from an ensemble of classifiers; and applying mixed precision training, which allows training neural networks with larger batch size. We experimented with loss functions inspired by the considered metrics: soft F1 loss and weighted cross entropy loss. However, the standard cross entropy loss achieved superior results both in accuracy and in F1 measures. The proposed method scored third in the SnakeCLEF 2021 challenge, achieving 91.6% classification accuracy, Country F1 Score of 0.860, and F1 Score of 0.830

Overview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem

The main goal of the new LifeCLEF challenge, FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem, was to provide an evaluation ground for end-to-end fungi species recognition in an open class set scenario. An AI-based fungi species recognition system deployed in the Atlas of Danish Fungi helps mycologists to collect valuable data and allows users to learn about fungi species identification. Advances in fungi recognition from images and metadata will allow continuous improvement of the system deployed in this citizen science project. The training set is based on the Danish Fungi 2020 dataset and contains 295,938 photographs of 1,604 species. For testing, we provided a collection of 59,420 expert-approved observations collected in 2021. The test set includes 1,165 species from the training set and 1,969 unknown species, leading to an open-set recognition problem. This paper provides (i) a description of the challenge task and datasets, (ii) a summary of the evaluation methodology, (iii) a review of the systems submitted by the participating teams, and (iv) a discussion of the challenge results. © 2022 Copyright for this paper by its authors

Automatic Fungi Recognition: Deep Learning Meets Mycology

The article presents an AI-based fungi species recognition system for a citizen-science community. The system’s real-time identification too — FungiVision — with a mobile application front-end, led to increased public interest in fungi, quadrupling the number of citizens collecting data. FungiVision, deployed with a human-in-the-loop, reaches nearly 93% accuracy. Using the collected data, we developed a novel fine-grained classification dataset — Danish Fungi 2020 (DF20) — with several unique characteristics: species-level labels, a small number of errors, and rich observation metadata. The dataset enables the testing of the ability to improve classification using metadata, e.g., time, location, habitat and substrate, facilitates classifier calibration testing and finally allows the study of the impact of the device settings on the classification performance. The continual flow of labelled data supports improvements of the online recognition system. Finally, we present a novel method for the fungi recognition service, based on a Vision Transformer architecture. Trained on DF20 and exploiting available metadata, it achieves a recognition error that is 46.75% lower than the current system. By providing a stream of labeled data in one direction, and an accuracy increase in the other, the collaboration creates a virtuous cycle helping both communities

Danish Fungi 2020 - Not Just Another Image Recognition Dataset

We introduce a novel fine-grained dataset and bench-mark, the Danish Fungi 2020 (DF20). The dataset, constructed from observations submitted to the Atlas of Danish Fungi, is unique in its taxonomy-accurate class labels, small number of errors, highly unbalanced long-tailed class distribution, rich observation metadata, and well-defined class hierarchy. DF20 has zero overlap with ImageNet, al-lowing unbiased comparison of models fine-tuned from publicly available ImageNet checkpoints. The proposed evaluation protocol enables testing the ability to improve classification using metadata - e.g. precise geographic location, habitat, and substrate, facilitates classifier calibration testing, and finally allows to study the impact of the device settings on the classification performance. Experiments using Convolutional Neural Networks (CNN) and the recent Vision Transformers (ViT) show that DF20 presents a challenging task. Interestingly, ViT achieves results su-perior to CNN baselines with 80.45% accuracy and 0.743 macro F1 score, reducing the CNN error by 9% and 12% respectively. A simple procedure for including metadata into the decision process improves the classification accuracy by more than 2.95 percentage points, reducing the error rate by 15%. The source code for all methods and experiments is available at https://sites.google.com/view/danish-fungi-dataset

Myomedin replicas of gp120 V3 loop glycan epitopes recognized by PGT121 and PGT126 antibodies as non-cognate antigens for stimulation of HIV-1 broadly neutralizing antibodies

IntroductionImprinting broadly neutralizing antibody (bNAb) paratopes by shape complementary protein mimotopes represents a potential alternative for developing vaccine immunogens. This approach, designated as a Non-Cognate Ligand Strategy (NCLS), has recently been used for the identification of protein variants mimicking CD4 binding region epitope or membrane proximal external region (MPER) epitope of HIV-1 envelope (Env) glycoprotein. However, the potential of small binding proteins to mimic viral glycan-containing epitopes has not yet been verified.MethodsIn this work, we employed a highly complex combinatorial Myomedin scaffold library to identify variants recognizing paratopes of super candidate bNAbs, PGT121 and PGT126, specific for HIV-1 V3 loop epitopes.ResultsIn the collection of Myomedins called MLD variants targeted to PGT121, three candidates competed with gp120 for binding to this bNAb in ELISA, thus suggesting an overlapping binding site and epitope-mimicking potential. Myomedins targeted to PGT126 designated MLB also provided variants that competed with gp120. Immunization of mice with MLB or MLD binders resulted in the production of anti-gp120 and -Env serum antibodies. Mouse hyper-immune sera elicited with MLB036, MLB041, MLB049, and MLD108 moderately neutralized 8-to-10 of 22 tested HIV-1-pseudotyped viruses of A, B, and C clades in vitro.DiscussionOur data demonstrate that Myomedin-derived variants can mimic particular V3 glycan epitopes of prominent anti-HIV-1 bNAbs, ascertain the potential of particular glycans controlling neutralizing sensitivity of individual HIV-1 pseudoviruses, and represent promising prophylactic candidates for HIV-1 vaccine development

Prognóza rozpočtu města Letohrad

Import 20/04/2006Prezenční výpůjčkaVŠB - Technická univerzita Ostrava. Ekonomická fakulta. Katedra (153) veřejné ekonomik

Fine-grained recognition of plants from images

Abstract Background Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition “in the wild”. Results We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition “in the wild”. Conclusions The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition “in the wild” where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter

Rozpoznávání Amazonské flóry pomocí Incepčních Sítí a odhadování apriorní pravděpodobnosti na testovacích obrazech

Článek popisuje automatický systém na rozpoznávání 10,000 druhů rostlin, se zaměřením na Guyanskou vysočinu a Amazonský deštný prales. Navrhovaný systém dosáhl nejlepších výsledků - 31,9% úspěšnosti na PlantCLEF2019 testovací sadě. Při porovnání s lidskými experty na rozpoznávání rostlin jsme dosáhli lepších výsledků než 3 z 5 expertů. Navrhovaný systém je založen na konvolučních neuronových sítích, respektive na architekturách Inception-v4 and Inception-ResNet-v2. Zlepšení výsledků bylo dosaženo především: úpravou predikcí neuronové sítě založené na odhadu apriorní pravděpodobnosti jednotlivých tříd, nahrazením parametrů sítě pomocí jejich klouzavého průměru, augmentací dat při testování, filtrováním trénovací množiny a rozšířením množiny trénovacích dat z knihovnz GBIF.The paper describes an automatic system for recognition of 10,000 plant species, with focus on species from the Guiana shield and the Amazon rain forest. The proposed system achieves the best results on the PlantCLEF 2019 test set with 31.9% accuracy. Compared against human experts in plant recognition, the system performed better than 3 of the 5 participating human experts and achieved 41.0% accuracy on the subset for expert evaluation. The proposed system is based on the Inception-v4 and Inception-ResNet-v2 Convolutional Neural Network (CNN) architectures. Performance improvements were achieved by: adjusting the CNN predictions according to the estimated change of the class prior probabilities, replacing network parameters with their running averages, test-time data augmentation, filtering the provided training set and adding additional training images from GBIF