16 research outputs found
MIDV-2019: Challenges of the modern mobile-based document OCR
Recognition of identity documents using mobile devices has become a topic of
a wide range of computer vision research. The portfolio of methods and
algorithms for solving such tasks as face detection, document detection and
rectification, text field recognition, and other, is growing, and the scarcity
of datasets has become an important issue. One of the openly accessible
datasets for evaluating such methods is MIDV-500, containing video clips of 50
identity document types in various conditions. However, the variability of
capturing conditions in MIDV-500 did not address some of the key issues, mainly
significant projective distortions and different lighting conditions. In this
paper we present a MIDV-2019 dataset, containing video clips shot with modern
high-resolution mobile cameras, with strong projective distortions and with low
lighting conditions. The description of the added data is presented, and
experimental baselines for text field recognition in different conditions. The
dataset is available for download at
ftp://smartengines.com/midv-500/extra/midv-2019/.Comment: 6 pages, 3 figures, 3 tables, 18 references, submitted and accepted
to the 12th International Conference on Machine Vision (ICMV 2019
HoughNet: neural network architecture for vanishing points detection
In this paper we introduce a novel neural network architecture based on Fast
Hough Transform layer. The layer of this type allows our neural network to
accumulate features from linear areas across the entire image instead of local
areas. We demonstrate its potential by solving the problem of vanishing points
detection in the images of documents. Such problem occurs when dealing with
camera shots of the documents in uncontrolled conditions. In this case, the
document image can suffer several specific distortions including projective
transform. To train our model, we use MIDV-500 dataset and provide testing
results. The strong generalization ability of the suggested method is proven
with its applying to a completely different ICDAR 2011 dewarping contest. In
previously published papers considering these dataset authors measured the
quality of vanishing point detection by counting correctly recognized words
with open OCR engine Tesseract. To compare with them, we reproduce this
experiment and show that our method outperforms the state-of-the-art result.Comment: 6 pages, 6 figures, 2 tables, 28 references, conferenc
MIDV-2020: A Comprehensive Benchmark Dataset for Identity Document Analysis
Identity documents recognition is an important sub-field of document
analysis, which deals with tasks of robust document detection, type
identification, text fields recognition, as well as identity fraud prevention
and document authenticity validation given photos, scans, or video frames of an
identity document capture. Significant amount of research has been published on
this topic in recent years, however a chief difficulty for such research is
scarcity of datasets, due to the subject matter being protected by security
requirements. A few datasets of identity documents which are available lack
diversity of document types, capturing conditions, or variability of document
field values. In addition, the published datasets were typically designed only
for a subset of document recognition problems, not for a complex identity
document analysis. In this paper, we present a dataset MIDV-2020 which consists
of 1000 video clips, 2000 scanned images, and 1000 photos of 1000 unique mock
identity documents, each with unique text field values and unique artificially
generated faces, with rich annotation. For the presented benchmark dataset
baselines are provided for such tasks as document location and identification,
text fields recognition, and face detection. With 72409 annotated images in
total, to the date of publication the proposed dataset is the largest publicly
available identity documents dataset with variable artificially generated data,
and we believe that it will prove invaluable for advancement of the field of
document analysis and recognition. The dataset is available for download at
ftp://smartengines.com/midv-2020 and http://l3i-share.univ-lr.fr