45 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
Recognition of off-line printed Arabic text using Hidden Markov Models.
yesThis paper describes a technique for automatic recognition of off-line printed Arabic text using Hidden Markov Models. In this work different sizes of overlapping and non-overlapping hierarchical windows are used to generate 16 features from each vertical sliding strip. Eight different Arabic fonts were used for testing (viz. Arial, Tahoma, Akhbar, Thuluth, Naskh, Simplified Arabic, Andalus, and Traditional Arabic). It was experimentally proven that different fonts have their highest recognition rates at different numbers of states (5 or 7) and codebook sizes (128 or 256).
Arabic text is cursive, and each character may have up to four different shapes based on its location in a word. This research work considered each shape as a different class, resulting in a total of 126 classes (compared to 28 Arabic letters). The achieved average recognition rates were between 98.08% and 99.89% for the eight experimental fonts.
The main contributions of this work are the novel hierarchical sliding window technique using only 16 features for each sliding window, considering each shape of Arabic characters as a separate class, bypassing the need for segmenting Arabic text, and its applicability to other languages
A Novel Dataset for English-Arabic Scene Text Recognition (EASTR)-42K and Its Evaluation Using Invariant Feature Extraction on Detected Extremal Regions
© 2019 IEEE. The recognition of text in natural scene images is a practical yet challenging task due to the large variations in backgrounds, textures, fonts, and illumination. English as a secondary language is extensively used in Gulf countries along with Arabic script. Therefore, this paper introduces English-Arabic scene text recognition 42K scene text image dataset. The dataset includes text images appeared in English and Arabic scripts while maintaining the prime focus on Arabic script. The dataset can be employed for the evaluation of text segmentation and recognition task. To provide an insight to other researchers, experiments have been carried out on the segmentation and classification of Arabic as well as English text and report error rates like 5.99% and 2.48%, respectively. This paper presents a novel technique by using adapted maximally stable extremal region (MSER) technique and extracts scale-invariant features from MSER detected region. To select discriminant and comprehensive features, the size of invariant features is restricted and considered those specific features which exist in the extremal region. The adapted MDLSTM network is presented to tackle the complexities of cursive scene text. The research on Arabic scene text is in its infancy, thus this paper presents benchmark work in the field of text analysis
UTRNet: High-Resolution Urdu Text Recognition In Printed Documents
In this paper, we propose a novel approach to address the challenges of
printed Urdu text recognition using high-resolution, multi-scale semantic
feature extraction. Our proposed UTRNet architecture, a hybrid CNN-RNN model,
demonstrates state-of-the-art performance on benchmark datasets. To address the
limitations of previous works, which struggle to generalize to the intricacies
of the Urdu script and the lack of sufficient annotated real-world data, we
have introduced the UTRSet-Real, a large-scale annotated real-world dataset
comprising over 11,000 lines and UTRSet-Synth, a synthetic dataset with 20,000
lines closely resembling real-world and made corrections to the ground truth of
the existing IIITH dataset, making it a more reliable resource for future
research. We also provide UrduDoc, a benchmark dataset for Urdu text line
detection in scanned documents. Additionally, we have developed an online tool
for end-to-end Urdu OCR from printed documents by integrating UTRNet with a
text detection model. Our work not only addresses the current limitations of
Urdu OCR but also paves the way for future research in this area and
facilitates the continued advancement of Urdu OCR technology. The project page
with source code, datasets, annotations, trained models, and online tool is
available at abdur75648.github.io/UTRNet.Comment: Accepted at The 17th International Conference on Document Analysis
and Recognition (ICDAR 2023
Field typing for improved recognition on heterogeneous handwritten forms
Offline handwriting recognition has undergone continuous progress over the
past decades. However, existing methods are typically benchmarked on free-form
text datasets that are biased towards good-quality images and handwriting
styles, and homogeneous content. In this paper, we show that state-of-the-art
algorithms, employing long short-term memory (LSTM) layers, do not readily
generalize to real-world structured documents, such as forms, due to their
highly heterogeneous and out-of-vocabulary content, and to the inherent
ambiguities of this content. To address this, we propose to leverage the
content type within an LSTM-based architecture. Furthermore, we introduce a
procedure to generate synthetic data to train this architecture without
requiring expensive manual annotations. We demonstrate the effectiveness of our
approach at transcribing text on a challenging, real-world dataset of European
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