40,662 research outputs found
Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter
Arabic is one of the languages that present special challenges to Optical
character recognition (OCR). The main challenge in Arabic is that it is mostly
cursive. Therefore, a segmentation process must be carried out to determine
where the character begins and where it ends. This step is essential for
character recognition. This paper presents Arabic character segmentation
algorithm. The proposed algorithm uses the projection-based approach concepts
to separate lines, words, and characters. This is done using profile's
amplitude filter and simple edge tool to find characters separations. Our
algorithm shows promising performance when applied on different machine printed
documents with different Arabic fonts
Large Vocabulary Arabic Online Handwriting Recognition System
Arabic handwriting is a consonantal and cursive writing. The analysis of
Arabic script is further complicated due to obligatory dots/strokes that are
placed above or below most letters and usually written delayed in order. Due to
ambiguities and diversities of writing styles, recognition systems are
generally based on a set of possible words called lexicon. When the lexicon is
small, recognition accuracy is more important as the recognition time is
minimal. On the other hand, recognition speed as well as the accuracy are both
critical when handling large lexicons. Arabic is rich in morphology and syntax
which makes its lexicon large. Therefore, a practical online handwriting
recognition system should be able to handle a large lexicon with reasonable
performance in terms of both accuracy and time. In this paper, we introduce a
fully-fledged Hidden Markov Model (HMM) based system for Arabic online
handwriting recognition that provides solutions for most of the difficulties
inherent in recognizing the Arabic script. A new preprocessing technique for
handling the delayed strokes is introduced. We use advanced modeling techniques
for building our recognition system from the training data to provide more
detailed representation for the differences between the writing units, minimize
the variances between writers in the training data and have a better
representation for the features space. System results are enhanced using an
additional post-processing step with a higher order language model and
cross-word HMM models. The system performance is evaluated using two different
databases covering small and large lexicons. Our system outperforms the
state-of-art systems for the small lexicon database. Furthermore, it shows
promising results (accuracy and time) when supporting large lexicon with the
possibility for adapting the models for specific writers to get even better
results.Comment: Preprint submitted to Pattern Analysis and Applications Journa
Arabic Text Recognition in Video Sequences
In this paper, we propose a robust approach for text extraction and
recognition from Arabic news video sequence. The text included in video
sequences is an important needful for indexing and searching system. However,
this text is difficult to detect and recognize because of the variability of
its size, their low resolution characters and the complexity of the
backgrounds. To solve these problems, we propose a system performing in two
main tasks: extraction and recognition of text. Our system is tested on a
varied database composed of different Arabic news programs and the obtained
results are encouraging and show the merits of our approach.Comment: 10 pages - International Journal of Computational Linguistics
Research. arXiv admin note: substantial text overlap with arXiv:1211.215
MIT-QCRI Arabic Dialect Identification System for the 2017 Multi-Genre Broadcast Challenge
In order to successfully annotate the Arabic speech con- tent found in
open-domain media broadcasts, it is essential to be able to process a diverse
set of Arabic dialects. For the 2017 Multi-Genre Broadcast challenge (MGB-3)
there were two possible tasks: Arabic speech recognition, and Arabic Dialect
Identification (ADI). In this paper, we describe our efforts to create an ADI
system for the MGB-3 challenge, with the goal of distinguishing amongst four
major Arabic dialects, as well as Modern Standard Arabic. Our research fo-
cused on dialect variability and domain mismatches between the training and
test domain. In order to achieve a robust ADI system, we explored both Siamese
neural network models to learn similarity and dissimilarities among Arabic
dialects, as well as i-vector post-processing to adapt domain mismatches. Both
Acoustic and linguistic features were used for the final MGB-3 submissions,
with the best primary system achieving 75% accuracy on the official 10hr test
set.Comment: Submitted to the 2017 IEEE Automatic Speech Recognition and
Understanding Workshop (ASRU 2017
A Hybrid NN/HMM Modeling Technique for Online Arabic Handwriting Recognition
In this work we propose a hybrid NN/HMM model for online Arabic handwriting
recognition. The proposed system is based on Hidden Markov Models (HMMs) and
Multi Layer Perceptron Neural Networks (MLPNNs). The input signal is segmented
to continuous strokes called segments based on the Beta-Elliptical strategy by
inspecting the extremum points of the curvilinear velocity profile. A neural
network trained with segment level contextual information is used to extract
class character probabilities. The output of this network is decoded by HMMs to
provide character level recognition. In evaluations on the ADAB database, we
achieved 96.4% character recognition accuracy that is statistically
significantly important in comparison with character recognition accuracies
obtained from state-of-the-art online Arabic systems.
UnibucKernel Reloaded: First Place in Arabic Dialect Identification for the Second Year in a Row
We present a machine learning approach that ranked on the first place in the
Arabic Dialect Identification (ADI) Closed Shared Tasks of the 2018 VarDial
Evaluation Campaign. The proposed approach combines several kernels using
multiple kernel learning. While most of our kernels are based on character
p-grams (also known as n-grams) extracted from speech or phonetic transcripts,
we also use a kernel based on dialectal embeddings generated from audio
recordings by the organizers. In the learning stage, we independently employ
Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR).
Preliminary experiments indicate that KRR provides better classification
results. Our approach is shallow and simple, but the empirical results obtained
in the 2018 ADI Closed Shared Task prove that it achieves the best performance.
Furthermore, our top macro-F1 score (58.92%) is significantly better than the
second best score (57.59%) in the 2018 ADI Shared Task, according to the
statistical significance test performed by the organizers. Nevertheless, we
obtain even better post-competition results (a macro-F1 score of 62.28%) using
the audio embeddings released by the organizers after the competition. With a
very similar approach (that did not include phonetic features), we also ranked
first in the ADI Closed Shared Tasks of the 2017 VarDial Evaluation Campaign,
surpassing the second best method by 4.62%. We therefore conclude that our
multiple kernel learning method is the best approach to date for Arabic dialect
identification.Comment: This paper presents the UnibucKernel team's participation at the 2018
Arabic Dialect Identification Shared Task. Accepted at the VarDial Workshop
of COLING 201
Neural Computing for Online Arabic Handwriting Character Recognition using Hard Stroke Features Mining
Online Arabic cursive character recognition is still a big challenge due to
the existing complexities including Arabic cursive script styles, writing
speed, writer mood and so forth. Due to these unavoidable constraints, the
accuracy of online Arabic character's recognition is still low and retain space
for improvement. In this research, an enhanced method of detecting the desired
critical points from vertical and horizontal direction-length of handwriting
stroke features of online Arabic script recognition is proposed. Each extracted
stroke feature divides every isolated character into some meaningful pattern
known as tokens. A minimum feature set is extracted from these tokens for
classification of characters using a multilayer perceptron with a
back-propagation learning algorithm and modified sigmoid function-based
activation function. In this work, two milestones are achieved; firstly, attain
a fixed number of tokens, secondly, minimize the number of the most repetitive
tokens. For experiments, handwritten Arabic characters are selected from the
OHASD benchmark dataset to test and evaluate the proposed method. The proposed
method achieves an average accuracy of 98.6% comparable in state of art
character recognition techniques.Comment: 16 page
A multi-stream hmm approach to offline handwritten arabic word recognition
In This paper we presented new approach for cursive Arabic text recognition
system. The objective is to propose methodology analytical offline recognition
of handwritten Arabic for rapid implementation. The first part in the writing
recognition system is the preprocessing phase is the preprocessing phase to
prepare the data was introduces and extracts a set of simple statistical
features by two methods : from a window which is sliding long that text line
the right to left and the approach VH2D (consists in projecting every character
on the abscissa, on the ordinate and the diagonals 45{\deg} and 135{\deg}) . It
then injects the resulting feature vectors to Hidden Markov Model (HMM) and
combined the two HMM by multi-stream approach.Comment: 12 pages,13 figure,International Journal on Natural Language
Computing(IJNLC),ISSN:2278-1307[Online];2319-4111[Print],August 2013, Volume
2, Number
OCR Error Correction Using Character Correction and Feature-Based Word Classification
This paper explores the use of a learned classifier for post-OCR text
correction. Experiments with the Arabic language show that this approach, which
integrates a weighted confusion matrix and a shallow language model, improves
the vast majority of segmentation and recognition errors, the most frequent
types of error on our dataset.Comment: Proceedings of the 12th IAPR International Workshop on Document
Analysis Systems (DAS2016), Santorini, Greece, April 11-14, 201
E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text
An end-to-end trainable (fully differentiable) method for multi-language
scene text localization and recognition is proposed. The approach is based on a
single fully convolutional network (FCN) with shared layers for both tasks.
E2E-MLT is the first published multi-language OCR for scene text. While
trained in multi-language setup, E2E-MLT demonstrates competitive performance
when compared to other methods trained for English scene text alone. The
experiments show that obtaining accurate multi-language multi-script
annotations is a challenging problem
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