2,903 research outputs found
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
Text Line Segmentation of Historical Documents: a Survey
There is a huge amount of historical documents in libraries and in various
National Archives that have not been exploited electronically. Although
automatic reading of complete pages remains, in most cases, a long-term
objective, tasks such as word spotting, text/image alignment, authentication
and extraction of specific fields are in use today. For all these tasks, a
major step is document segmentation into text lines. Because of the low quality
and the complexity of these documents (background noise, artifacts due to
aging, interfering lines),automatic text line segmentation remains an open
research field. The objective of this paper is to present a survey of existing
methods, developed during the last decade, and dedicated to documents of
historical interest.Comment: 25 pages, submitted version, To appear in International Journal on
Document Analysis and Recognition, On line version available at
http://www.springerlink.com/content/k2813176280456k3
Component-based Segmentation of words from handwritten Arabic text
Efficient preprocessing is very essential for automatic recognition of handwritten documents. In this paper, techniques on segmenting words in handwritten Arabic text are presented. Firstly, connected components (ccs) are extracted, and distances among different components are analyzed. The statistical distribution of this distance is then obtained to determine an optimal threshold for words segmentation. Meanwhile, an improved projection based method is also employed for baseline detection. The proposed method has been successfully tested on IFN/ENIT database consisting of 26459 Arabic words handwritten by 411 different writers, and the results were promising and very encouraging in more accurate detection of the baseline and segmentation of words for further recognition
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
Deep LSTM is an ideal candidate for text recognition. However text
recognition involves some initial image processing steps like segmentation of
lines and words which can induce error to the recognition system. Without
segmentation, learning very long range context is difficult and becomes
computationally intractable. Therefore, alternative soft decisions are needed
at the pre-processing level. This paper proposes a hybrid text recognizer using
a deep recurrent neural network with multiple layers of abstraction and long
range context along with a language model to verify the performance of the deep
neural network. In this paper we construct a multi-hypotheses tree architecture
with candidate segments of line sequences from different segmentation
algorithms at its different branches. The deep neural network is trained on
perfectly segmented data and tests each of the candidate segments, generating
unicode sequences. In the verification step, these unicode sequences are
validated using a sub-string match with the language model and best first
search is used to find the best possible combination of alternative hypothesis
from the tree structure. Thus the verification framework using language models
eliminates wrong segmentation outputs and filters recognition errors
- …