3,693 research outputs found
Implicit Language Model in LSTM for OCR
Neural networks have become the technique of choice for OCR, but many aspects
of how and why they deliver superior performance are still unknown. One key
difference between current neural network techniques using LSTMs and the
previous state-of-the-art HMM systems is that HMM systems have a strong
independence assumption. In comparison LSTMs have no explicit constraints on
the amount of context that can be considered during decoding. In this paper we
show that they learn an implicit LM and attempt to characterize the strength of
the LM in terms of equivalent n-gram context. We show that this implicitly
learned language model provides a 2.4\% CER improvement on our synthetic test
set when compared against a test set of random characters (i.e. not naturally
occurring sequences), and that the LSTM learns to use up to 5 characters of
context (which is roughly 88 frames in our configuration). We believe that this
is the first ever attempt at characterizing the strength of the implicit LM in
LSTM based OCR systems
Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers
The massive amounts of digitized historical documents acquired over the last
decades naturally lend themselves to automatic processing and exploration.
Research work seeking to automatically process facsimiles and extract
information thereby are multiplying with, as a first essential step, document
layout analysis. If the identification and categorization of segments of
interest in document images have seen significant progress over the last years
thanks to deep learning techniques, many challenges remain with, among others,
the use of finer-grained segmentation typologies and the consideration of
complex, heterogeneous documents such as historical newspapers. Besides, most
approaches consider visual features only, ignoring textual signal. In this
context, we introduce a multimodal approach for the semantic segmentation of
historical newspapers that combines visual and textual features. Based on a
series of experiments on diachronic Swiss and Luxembourgish newspapers, we
investigate, among others, the predictive power of visual and textual features
and their capacity to generalize across time and sources. Results show
consistent improvement of multimodal models in comparison to a strong visual
baseline, as well as better robustness to high material variance
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
This work addresses the problem of vehicle identification through
non-overlapping cameras. As our main contribution, we introduce a novel dataset
for vehicle identification, called Vehicle-Rear, that contains more than three
hours of high-resolution videos, with accurate information about the make,
model, color and year of nearly 3,000 vehicles, in addition to the position and
identification of their license plates. To explore our dataset we design a
two-stream CNN that simultaneously uses two of the most distinctive and
persistent features available: the vehicle's appearance and its license plate.
This is an attempt to tackle a major problem: false alarms caused by vehicles
with similar designs or by very close license plate identifiers. In the first
network stream, shape similarities are identified by a Siamese CNN that uses a
pair of low-resolution vehicle patches recorded by two different cameras. In
the second stream, we use a CNN for OCR to extract textual information,
confidence scores, and string similarities from a pair of high-resolution
license plate patches. Then, features from both streams are merged by a
sequence of fully connected layers for decision. In our experiments, we
compared the two-stream network against several well-known CNN architectures
using single or multiple vehicle features. The architectures, trained models,
and dataset are publicly available at https://github.com/icarofua/vehicle-rear
Unconstrained Scene Text and Video Text Recognition for Arabic Script
Building robust recognizers for Arabic has always been challenging. We
demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid
architecture in recognizing Arabic text in videos and natural scenes. We
outperform previous state-of-the-art on two publicly available video text
datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a
new Arabic scene text dataset and establish baseline results. For scripts like
Arabic, a major challenge in developing robust recognizers is the lack of large
quantity of annotated data. We overcome this by synthesising millions of Arabic
text images from a large vocabulary of Arabic words and phrases. Our
implementation is built on top of the model introduced here [37] which is
proven quite effective for English scene text recognition. The model follows a
segmentation-free, sequence to sequence transcription approach. The network
transcribes a sequence of convolutional features from the input image to a
sequence of target labels. This does away with the need for segmenting input
image into constituent characters/glyphs, which is often difficult for Arabic
script. Further, the ability of RNNs to model contextual dependencies yields
superior recognition results.Comment: 5 page
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