112 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
Sub-sampling Approach for Unconstrained Arabic Scene Text Analysis by Implicit Segmentation based Deep Learning Classifier
The text extraction from the natural scene image is still a cumbersome task to perform. This paper presents a novel contribution and suggests the solution for cursive scene text analysis notably recognition of Arabic scene text appeared in the unconstrained environment. The hierarchical sub-sampling technique is adapted to investigate the potential through sub-sampling the window size of the given scene text sample. The deep learning architecture is presented by considering the complexity of the Arabic script. The conducted experiments present 96.81% accuracy at the character level. The comparison of the Arabic scene text with handwritten and printed data is outlined as well
Evaluation of handwritten Urdu text by integration of MNIST dataset learning experience
© 2019 IEEE. The similar nature of patterns may enhance the learning if the experience they attained during training is utilized to achieve maximum accuracy. This paper presents a novel way to exploit the transfer learning experience of similar patterns on handwritten Urdu text analysis. The MNIST pre-trained network is employed by transferring it's learning experience on Urdu Nastaliq Handwritten Dataset (UNHD) samples. The convolutional neural network is used for feature extraction. The experiments were performed using deep multidimensional long short term (MDLSTM) memory networks. The obtained result shows immaculate performance on number of experiments distinguished on the basis of handwritten complexity. The result of demonstrated experiments show that pre-trained network outperforms on subsequent target networks which enable them to focus on a particular feature learning. The conducted experiments presented astonishingly good accuracy on UNHD dataset
Arabic cursive text recognition from natural scene images
© 2019 by the authors. This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years' publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers
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
Scene Based Text Recognition From Natural Images and Classification Based on Hybrid CNN Models with Performance Evaluation
Similar to the recognition of captions, pictures, or overlapped text that typically appears horizontally, multi-oriented text recognition in video frames is challenging since it has high contrast related to its background. Multi-oriented form of text normally denotes scene text which makes text recognition further stimulating and remarkable owing to the disparaging features of scene text. Hence, predictable text detection approaches might not give virtuous outcomes for multi-oriented scene text detection. Text detection from any such natural image has been challenging since earlier times, and significant enhancement has been made recently to execute this task. While coming to blurred, low-resolution, and small-sized images, most of the previous research conducted doesn’t work well; hence, there is a research gap in that area. Scene-based text detection is a key area due to its adverse applications. One such primary reason for the failure of earlier methods is that the existing methods could not generate precise alignments across feature areas and targets for those images. This research focuses on scene-based text detection with the aid of YOLO based object detector and a CNN-based classification approach. The experiments were conducted in MATLAB 2019A, and the packages used were RESNET50, INCEPTIONRESNETV2, and DENSENET201. The efficiency of the proposed methodology - Hybrid resnet -YOLO procured maximum accuracy of 91%, Hybrid inceptionresnetv2 -YOLO of 81.2%, and Hybrid densenet201 -YOLO of 83.1% and was verified by comparing it with the existing research works Resnet50 of 76.9%, ResNet-101 of 79.5%, and ResNet-152 of 82%
Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey
Optical character recognition (OCR) is a vital process that involves the
extraction of handwritten or printed text from scanned or printed images,
converting it into a format that can be understood and processed by machines.
This enables further data processing activities such as searching and editing.
The automatic extraction of text through OCR plays a crucial role in digitizing
documents, enhancing productivity, improving accessibility, and preserving
historical records. This paper seeks to offer an exhaustive review of
contemporary applications, methodologies, and challenges associated with Arabic
Optical Character Recognition (OCR). A thorough analysis is conducted on
prevailing techniques utilized throughout the OCR process, with a dedicated
effort to discern the most efficacious approaches that demonstrate enhanced
outcomes. To ensure a thorough evaluation, a meticulous keyword-search
methodology is adopted, encompassing a comprehensive analysis of articles
relevant to Arabic OCR, including both backward and forward citation reviews.
In addition to presenting cutting-edge techniques and methods, this paper
critically identifies research gaps within the realm of Arabic OCR. By
highlighting these gaps, we shed light on potential areas for future
exploration and development, thereby guiding researchers toward promising
avenues in the field of Arabic OCR. The outcomes of this study provide valuable
insights for researchers, practitioners, and stakeholders involved in Arabic
OCR, ultimately fostering advancements in the field and facilitating the
creation of more accurate and efficient OCR systems for the Arabic language
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