Optical Character Recognition Approach Research Articles

A Novel Arabic Optical Character Recognition Approach Based on Levenshtein Distance

A Novel Arabic Optical Character Recognition Approach Based on Levenshtein Distance

Egyptian car plate recognition based on YOLOv8, Easy-OCR, and CNN

This research presents an innovative approach to Egyptian car plate recognition using YOLOv8 and optical character recognition (OCR) technologies. Leveraging the powerful object detection capabilities of YOLOv8, the system efficiently detects car plates within images, videos, or real-time. Subsequently, OCR algorithms are applied to extract alphanumeric characters from the identified plates, facilitating accurate license plate recognition. The integration of YOLOv8 and OCR enhances the system's robustness in varying conditions, contributing to improved performance in real-world scenarios. This study advances the field of automatic license plate recognition, showcasing the potential for practical applications in traffic management, law enforcement, and security systems. A public dataset of Egyptian car plates is used for training and testing the model. Two OCR approaches are used and tested which proved their performance, while CNN-based approach reaches 99.4% accuracy.

A Deep Neural Network Based Holistic Approach for Optical Character Recognition of Handwritten Documents

A Deep Neural Network Based Holistic Approach for Optical Character Recognition of Handwritten Documents

Optical Character Recognition Development Using Python

Optical Character Recognition (OCR) is a technology used to convert scanned or digital images into editable text. OCR has become an increasingly important tool in the fields of data extraction and information retrieval, allowing for quick and efficient conversion of scanned documents and digital images into text. In this paper, we explore the use of the Python programming language to implement OCR algorithms and systems. We provide a comprehensive overview of existing Python libraries and packages used for OCR, including Tesseract and pytesseract, along with their strengths and limitations. We also examine the different OCR approaches and techniques, including template matching, feature extraction, and encrypting/decrypting the OCR parsed files and discuss their implementation in Python. Finally, we present a case study of a simple OCR system built using Python and evaluate its performance on a sample dataset. The results of our study highlight the potential of Python for OCR implementation and demonstrate its feasibility for real-world applications.

New architectural optical character recognition approach for cursive fonts: the historical Maghrebian font as an example

New architectural optical character recognition approach for cursive fonts: the historical Maghrebian font as an example

New architectural optical character recognition approach for cursive fonts: the historical Maghrebian font as an example

New architectural optical character recognition approach for cursive fonts: the historical Maghrebian font as an example

Automatic number plate detection using TensorFlow in Indian scenario: An optical character recognition approach

India, a country of over 130 Crores population, witnessed an exponential rise in the number of vehicles running on the road. As per the survey made in the year 2019, in India there is one death in every 4 min occurring due to over speeding. Moreover, there are around 2 lakhs vehicles being stolen in India per Year. This number can be reduced to a greater extent if proper steps are taken. Automatic number plate detection is a very important tool when it comes to detecting the license plate of a vehicle from the image of a vehicle so taken. Moreover, same tool can be implemented in the case of over speed monitoring, smart parking management system, law enforcement and electronic toll collection. In this paper, work has been carried out to automatically detect the number plate of a vehicle using computer vision technique. TensorFlow, a model developed by Google is used for the purpose of training and testing of the algorithm. A data set of about 500 images obtained at the different condition of the illumination, camera angle and varied distances has been used while training and testing the model. This resulted in an improved accuracy of about 85% with which the license plate has been detected. Further optical character recognition is implemented in order to get the license number from the image being detected.

Optimal affine image normalization approach for optical character recognition

Optical character recognition (OCR) in images captured from arbitrary angles requires preliminary normalization, i.e. a geometric transformation resulting in an image as if it was captured at an angle suitable for OCR. In most cases, a surface containing characters can be considered flat, and a pinhole model can be adopted for a camera. Thus, in theory, the normalization should be projective. Usually, the camera optical axis is approximately perpendicular to the document surface, so the projective normalization can be replaced with an affine one without a significant loss of accuracy. An affine image transformation is performed significantly faster than a projective normalization, which is important for OCR on mobile devices. In this work, we propose a fast approach for image normalization. It utilizes an affine normalization instead of a projective one if there is no significant loss of accuracy. The approach is based on a proposed criterion for the normalization accuracy: root mean square (RMS) coordinate discrepancies over the region of interest (ROI). The problem of optimal affine normalization according to this criterion is considered. We have established that this unconstrained optimization is quadratic and can be reduced to a problem of fractional quadratic functions integration over the ROI. The latter was solved analytically in the case of OCR where the ROI consists of rectangles. The proposed approach is generalized for various cases when instead of the affine transform its special cases are used: scaling, translation, shearing, and their superposition, allowing the image normalization procedure to be further accelerated.

Handwritten Arabic Optical Character Recognition Approach Based on Hybrid Whale Optimization Algorithm With Neighborhood Rough Set

Accomplishing high recognition performance is considered one of the most important tasks for handwritten Arabic character recognition systems. In general, Optical Character Recognition (OCR) systems are constructed from four phases: pre-processing, feature extraction, feature selection, and classification. Recent literature focused on the selection of appropriate features as a key point towards building a successful and sufficient character recognition system. In this paper, we propose a hybrid machine learning approach that utilizes neighborhood rough sets with a binary whale optimization algorithm to select the most appropriate features for the recognition of handwritten Arabic characters. To validate the proposed approach, we used the CENPARMI dataset, which is a well-known dataset for machine learning experiments involving handwritten Arabic characters. The results show clear advantages of the proposed approach in terms of recognition accuracy, memory footprint, and processor time than those without the features of the proposed method. When comparing the results of the proposed method with other recent state-of-the-art optimization algorithms, the proposed approach outperformed all others in all experiments. Moreover, the proposed approach shows the highest recognition rate with the smallest consumption time compared to deep neural networks such as VGGnet, Resnet, Nasnet, Mobilenet, Inception, and Xception. The proposed approach was also compared with recently published works using the same dataset, which further confirmed the outstanding classification accuracy and time consumption of this approach. The misclassified failure cases were studied and analyzed, which showed that they would likely be confusing for even Arabic natives because the correct interpretation of the characters required the context of their appearance.

Enhancing RNN Based OCR by Transductive Transfer Learning From Text to Images

This paper presents a novel approach for optical character recognition (OCR) on acceleration and to avoid underfitting by text. Previously proposed OCR models typically take much time in the training phase and require large amount of labelled data to avoid underfitting. In contrast, our method does not require such condition. This is a challenging task related to transferring the character sequential relationship from text to OCR. We build a model based on transductive transfer learning to achieve domain adaptation from text to image. We thoroughly evaluate our approach on different datasets, including a general one and a relatively small one. We also compare the performance of our model with the general OCR model on different circumstances. We show that (1) our approach accelerates the training phase 20-30% on time cost; and (2) our approach can avoid underfitting while model is trained on a small dataset.

Experimental evaluation of Arabic OCR systems

Purpose The aim of this paper is to experimentally evaluate the effectiveness of the state-of-the-art printed Arabic text recognition systems to determine open areas for future improvements. In addition, this paper proposes a standard protocol with a set of metrics for measuring the effectiveness of Arabic optical character recognition (OCR) systems to assist researchers in comparing different Arabic OCR approaches. Design/methodology/approach This paper describes an experiment to automatically evaluate four well-known Arabic OCR systems using a set of performance metrics. The evaluation experiment is conducted on a publicly available printed Arabic dataset comprising 240 text images with a variety of resolution levels, font types, font styles and font sizes. Findings The experimental results show that the field of character recognition for printed Arabic still requires further research to reach an efficient text recognition method for Arabic script. Originality/value To the best of the authors’ knowledge, this is the first work that provides a comprehensive automated evaluation of Arabic OCR systems with respect to the characteristics of Arabic script and, in addition, proposes an evaluation methodology that can be used as a benchmark by researchers and therefore will contribute significantly to the enhancement of the field of Arabic script recognition.

Nastalique segmentation-based approach for Urdu OCR

Much work on Arabic language optical character recognition (OCR) has been on Naskh writing style. Nastalique style, used for most of languages using Arabic script across Southern Asia, is much more challenging to process due to its compactness, cursiveness, higher context sensitivity and diagonality. This makes the Nastalique writing more complex with multiple letters horizontally overlapping each other. Due to these reasons, existing methods used for Naskh would not work for Nastalique and therefore most work on Nastalique has used non-segmentation methods. The current paper presents new approach for segmentation-based analysis for Nastalique style. The paper explains the complexity of Nastalique, why Naskh based techniques cannot work for Nastalique, and proposes a segmentation-based method for developing Nastalique OCR, deriving principles and techniques for the pre-processing and recognition. The OCR is developed for Urdu language. The system is optimized using 79,093 instances of 5249 main bodies derived from a corpus of 18 million words, giving recognition accuracy of 97.11 %. The system is then tested on document images of books with 87.44 % main body recognition accuracy. The work is extensible to other languages using Nastalique.

A Novel Approach for Character Recognition

Abstract — “Optical Character Recognition”, abbreviated as OCR has become a very important aspect of life in today’s world especially in Computer vision applications, and also in the field of Robotics. Our basic objective is to develop an efficient approach which can recognize the input printed-text with a high level of accuracy so that its output can be utilized in our final goal which is an English-Text to multilingual Translator. In this paper the methodology adopted to implement OCR is mainly explained in detail along with emphasizing the necessity and advantages over the present technique. In this paper, an efficient approach for optical Character recognition of printed text has been analyzed. The problems associated with Character Recognition are also mentioned. A Graphical User Interface (GUI), in this paper represented for that purpose. Keywords — Optical Character Recognition, Computer-Vision, Robotics, Graphical User Interface.

Skew angle detection of digitized Indian script documents

Skew angle detection of scanned documents containing most popular Indian scripts (Devnagari and Bangla) is considered. Most characters in these scripts have horizontal lines at the top, called head lines. The character head lines mostly join one another in a word and the word appears as a single component. In the proposed method the components are at first labeled. The upper envelope of a component is found by columnwise scanning from an imaginary line above the component. Portions of upper envelope satisfying the properties of digital straight line are detected. They are clustered as belonging to single text lines. Estimates from individual clusters are combined to get the skew angle. Apart from accuracy and efficiency, an advantage of the method is that character segmentation and zone detection can be readily done from head line information, which is useful in optical character recognition approaches of these scripts.