23 research outputs found
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
Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques
The potential uses of cursive optical character recognition, commonly known as OCR, in a number of industries, particularly document digitization, archiving, even language preservation, have attracted a lot of interest lately. In the framework of optical character recognition (OCR), the goal of this research is to provide a thorough understanding of both cutting-edge methods and the unique difficulties presented by Indic scripts. A thorough literature search was conducted in order to conduct this study, during which time relevant publications, conference proceedings, and scientific files were looked for up to the year 2023. As a consequence of the inclusion criteria that were developed to concentrate on studies only addressing Handwritten OCR on Indic scripts, 53 research publications were chosen as the process's outcome. The review provides a thorough analysis of the methodology and approaches employed in the chosen study. Deep neural networks, conventional feature-based methods, machine learning techniques, and hybrid systems have all been investigated as viable answers to the problem of effectively deciphering Indian scripts, because they are famously challenging to write. To operate, these systems require pre-processing techniques, segmentation schemes, and language models. The outcomes of this methodical examination demonstrate that despite the fact that Hand Scanning for Indic script has advanced significantly, room still exists for advancement. Future research could focus on developing trustworthy models that can handle a range of writing styles and enhance accuracy using less-studied Indic scripts. This profession may advance with the creation of collected datasets and defined standards
A review of Arabic text recognition dataset
Building a robust Optical Character Recognition (OCR) system for languages, such as Arabic with cursive scripts,
has always been challenging. These challenges increase if the text contains diacritics of different sizes for
characters and words. Apart from the complexity of the used font, these challenges must be addressed in
recognizing the text of the Holy Quran. To solve these challenges, the OCR system would have to undergo
different phases. Each problem would have to be addressed using different approaches, thus, researchers are
studying these challenges and proposing various solutions. This has motivate this study to review Arabic OCR
dataset because the dataset plays a major role in determining the nature of the OCR systems. State-of-the-art
approaches in segmentation and recognition are discovered with the implementation of Recurrent Neural
Networks (Long Short-Term Memory-LSTM and Gated Recurrent Unit-GRU) with the use of the Connectionist
Temporal Classification (CTC). This also includes deep learning model and implementation of GRU in the Arabic
domain. This paper has contribute in profiling the Arabic text recognition dataset thus determining the nature of
OCR system developed and has identified research direction in building Arabic text recognition dataset
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
Ā© 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
A Study of Techniques and Challenges in Text Recognition Systems
The core system for Natural Language Processing (NLP) and digitalization is Text Recognition. These systems are critical in bridging the gaps in digitization produced by non-editable documents, as well as contributing to finance, health care, machine translation, digital libraries, and a variety of other fields. In addition, as a result of the pandemic, the amount of digital information in the education sector has increased, necessitating the deployment of text recognition systems to deal with it. Text Recognition systems worked on three different categories of text: (a) Machine Printed, (b) Offline Handwritten, and (c) Online Handwritten Texts. The major goal of this research is to examine the process of typewritten text recognition systems. The availability of historical documents and other traditional materials in many types of texts is another major challenge for convergence. Despite the fact that this research examines a variety of languages, the Gurmukhi language receives the most focus. This paper shows an analysis of all prior text recognition algorithms for the Gurmukhi language. In addition, work on degraded texts in various languages is evaluated based on accuracy and F-measure
Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network
This study was supported by the China University of Petroleum-Beijing and Fundamental Research Funds for Central Universities under Grant no. 2462020YJRC001.Peer reviewedPublisher PD
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