Visual appearance based document image classification

Usilin, Sergey A.; Nikolaev, Dmitry P.; Postnikov, Vassili V.; Schaefer, Gerald

doi:10.1109/icip.2010.5652024

Cited by 27 publications

(7 citation statements)

References 8 publications

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“…It raises the entry barrier, putting off those who don't have the resources to create their own datasets and slowing down those who haven't collected the data yet. Furthermore, it becomes impossible to evaluate and compare various identity document analysis methods to each other, since they have been tested on completely different and locked down data [27,25]. In addition, there is an ethical concern regarding verifiable and reproducible research, especially during peer review process.…”

Section: Introductionmentioning

confidence: 99%

MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream

Arlazarov

Bulatov

Chernov³

2019

Computer Optics

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A lot of research has been devoted to identity documents analysis and recognition on mobile devices. However, no publicly available datasets designed for this particular problem currently exist. There are a few datasets which are useful for associated subtasks but in order to facilitate a more comprehensive scientific and technical approach to identity documents recognition more specialized datasets are required. In this paper we present a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide This work is partially supported by Russian Foundation for Basic Research (projects 17-29-03170 and 17-29-03370). Source images for MIDV-500 datasets are obtained from Wikimedia Commons

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Section: Introductionmentioning

confidence: 99%

MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream

Arlazarov

Bulatov

Chernov³

2019

Computer Optics

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“…In contrast, we perform fast spatial pyramid matching which can be seen as an approximate and robust matching of descriptors. Usilin et al [14] perform document detection given an uncropped image based on the Viola-Jones detection framework. Sarkar [15] also uses the Haar-like features of the Viola-Jones-Framework and performs a maximum-likelihood estimation during classification.…”

Section: Related Workmentioning

confidence: 99%

Fine-grained classification of identity document types with only one example

Simon

Rodner

Denzler

2015

2015 14th IAPR International Conference on Machine Vision Applications (MVA)

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In this paper, we tackle the task of recognizing types of partly very similar identity documents using state-of-the-art visual recognition approaches. Given a scanned document, the goal is to identify the country of issue, the type of document, and its version. Whereas recognizing the individual parts of a document with known standardized layout can be done reliably, identifying the type of a document and therefore also its layout is a challenging problem due to the large variety of documents. In our paper, we develop and evaluate different techniques for this application including feature representations based on recent achievements with convolutional neural networks. On a dataset with 74 different classes and using only one training image per class, our best approach achieves a mean class-wise accuracy of 97.7%.

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“…Object detection is a computer vision technique which uses convolutional neural networks to identify the semantic objects in an image. Usilin [9] discussed the application of object detection for image classification. One of the approaches described in this paper uses an object detection method, but for app classification rather than image classification.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Usage of Images for App Classification

Singla

Mukherjee

Koduvely

et al. 2018

2018 15th IEEE India Council International Conference (INDICON)

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App classification is useful in a number of applications such as adding apps to an app store or building a user model based on the installed apps. Presently there are a number of existing methods to classify apps based on a given taxonomy on the basis of their text metadata. However, text based methods for app classification may not work in all cases, such as when the text descriptions are in a different language, or missing, or inadequate to classify the app. One solution in such cases is to utilize the app images to supplement the text description. In this paper, we evaluate a number of approaches in which app images can be used to classify the apps. In one approach, we use Optical character recognition (OCR) to extract text from images, which is then used to supplement the text description of the app. In another, we use pic2vec to convert the app images into vectors, then train an SVM to classify the vectors to the correct app label. In another, we use the captionbot.ai tool to generate natural language descriptions from the app images. Finally, we use a method to detect and label objects in the app images and use a voting technique to determine the category of the app based on all the images. We compare the performance of our image-based techniques to classify a number of apps in our dataset. We use a text based SVM app classifier as our base and obtained an improved classification accuracy of 96% for some classes when app images are added.

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Visual appearance based document image classification

Cited by 27 publications

References 8 publications

MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream

MIDV-500: a dataset for identity document analysis and recognition on mobile devices in video stream

Fine-grained classification of identity document types with only one example

Evaluating Usage of Images for App Classification

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