Recognition of Arabic handwritten words

Recognizing Arabic handwritten words is a difficult problem due to the deformations of different writing styles. Moreover, the cursive nature of the Arabic writing makes correct segmentation of characters an almost impossible task. While there are many sub systems in an Arabic words recognition system, in this work we develop a sub system to recognize Part of Arabic Words (PAW). We try to solve this problem using three different approaches, implicit segmentation and two variants of holistic approach. While Rothacker found similar conclusions while this work is being prepared, we report the difficulty in locating characters in PAW using Scale Invariant Feature Transforms under the first approach. In the second and third approaches, we use holistic approach to recognize PAW using Support Vector Machine (SVM) and Active Shape Models (ASM). While there are few works that use SVM to recognize PAW, they use a small dataset; we use a large dataset and a different set of features. We also explain the errors SVM and ASM make and propose some remedies to these errors as future work

Feature design and lexicon reduction for efficient offline handwriting recognition

This thesis establishes a pattern recognition framework for offline word recognition systems. It focuses on the image level features because they greatly influence the recognition performance. In particular, we consider two complementary aspects of prominent features impact: lexicon reduction and the actual recognition. The first aspect, lexicon reduction, consists in the design of a weak classifier which outputs a set of candidate word hypotheses given a word image. Its main purpose is to reduce the recognition computational time while maintaining (or even improving) the recognition rate. The second aspect is the actual recognition system itself. In fact, several features exist in the literature based on different fields of research, but no consensus exists concerning the most promising ones. The goal of the proposed framework is to improve our understanding of relevant features in order to build better recognition systems. For this purpose, we addressed two specific problems: 1) feature design for lexicon reduction (application to Arabic script), and 2) feature evaluation for cursive handwriting recognition (application to Latin and Arabic scripts). Few methods exist for lexicon reduction in Arabic script, unlike Latin script. Existing methods use salient features of Arabic words such as the number of subwords and diacritics, but totally ignore the shape of the subwords. Therefore, our first goal is to perform lexicon reductionn based on subwords shape. Our approach is based on shape indexing, where the shape of a query subword is compared to a labeled database of sample subwords. For efficient comparison with a low computational overhead, we proposed the weighted topological signature vector (W-TSV) framework, where the subword shape is modeled as a weighted directed acyclic graph (DAG) from which the W-TSV vector is extracted for efficient indexing. The main contributions of this work are to extend the existing TSV framework to weighted DAG and to propose a shape indexing approach for lexicon reduction. Good performance for lexicon reduction is achieved for Arabic subwords. Nevertheless, the performance remains modest for Arabic words. Considering the results of our first work on Arabic lexicon reduction, we propose to build a new index for better performance at the word level. The subword shape and the number of subwords and diacritics are all important components of Arabic word shape. We therefore propose the Arabic word descriptor (AWD) which integrates all the aforementioned components. It is built in two steps. First, a structural descriptor (SD) is computed for each connected component (CC) of the word image. It describes the CC shape using the bag-of-words model, where each visual word represents a different local shape structure. Then, the AWD is formed by concatenating the SDs using an efficient heuristic, implicitly discriminating between subwords and diacritics. In the context of lexicon reduction, the AWD is used to index a reference database. The main contribution of this work is the design of the AWD, which integrates lowlevel cues (subword shape structure) and symbolic information (subword counts and diacritics) into a single descriptor. The proposed method has a low computational overhead, it is simple to implement and it provides state-of-the-art performance for lexicon reduction on two Arabic databases, namely the Ibn Sina database of subwords and the IFN/ENIT database of words. The last part of this thesis focuses on features for word recognition. A large body of features exist in the literature, each of them being motivated by different fields, such as pattern recognition, computer vision or machine learning. Identifying the most promising approaches would improve the design of the next generation of features. Nevertheless, because they are based on different concepts, it is difficult to compare them on a theoretical ground and efficient empirical tools are needed. Therefore, the last objective of the thesis is to provide a method for feature evaluation that assesses the strength and complementarity of existing features. A combination scheme has been designed for this purpose, in which each feature is evaluated through a reference recognition system, based on recurrent neural networks. More precisely, each feature is represented by an agent, which is an instance of the recognition system trained with that feature. The decisions of all the agents are combined using a weighted vote. The weights are jointly optimized during a training phase in order to increase the weighted vote of the true word label. Therefore, they reflect the strength and complementarity of the agents and their features for the given task. Finally, they are converted into a numerical score assigned to each feature, which is easy to interpret under this combination model. To the best of our knowledge, this is the first feature evaluation method able to quantify the importance of each feature, instead of providing a ranking based on the recognition rate. Five state-of-the-art features have been tested, and our results provide interesting insight for future feature design

Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead

SEARCHING HETEROGENEOUS DOCUMENT IMAGE COLLECTIONS

A decrease in data storage costs and widespread use of scanning devices has led to massive quantities of scanned digital documents in corporations, organizations, and governments around the world. Automatically processing these large heterogeneous collections can be difficult due to considerable variation in resolution, quality, font, layout, noise, and content. In order to make this data available to a wide audience, methods for efficient retrieval and analysis from large collections of document images remain an open and important area of research. In this proposal, we present research in three areas that augment the current state of the art in the retrieval and analysis of large heterogeneous document image collections. First, we explore an efficient approach to document image retrieval, which allows users to perform retrieval against large image collections in a query-by-example manner. Our approach is compared to text retrieval of OCR on a collection of 7 million document images collected from lawsuits against tobacco companies. Next, we present research in document verification and change detection, where one may want to quickly determine if two document images contain any differences (document verification) and if so, to determine precisely what and where changes have occurred (change detection). A motivating example is legal contracts, where scanned images are often e-mailed back and forth and small changes can have severe ramifications. Finally, approaches useful for exploiting the biometric properties of handwriting in order to perform writer identification and retrieval in document images are examined

Ensemble learning using multi-objective optimisation for arabic handwritten words

Arabic handwriting recognition is a dynamic and stimulating field of study within pattern recognition. This system plays quite a significant part in today's global environment. It is a widespread and computationally costly function due to cursive writing, a massive number of words, and writing style. Based on the literature, the existing features lack data supportive techniques and building geometric features. Most ensemble learning approaches are based on the assumption of linear combination, which is not valid due to differences in data types. Also, the existing approaches of classifier generation do not support decision-making for selecting the most suitable classifier, and it requires enabling multi-objective optimisation to handle these differences in data types. In this thesis, new type of feature for handwriting using Segments Interpolation (SI) to find the best fitting line in each of the windows with a model for finding the best operating point window size for SI features. Multi-Objective Ensemble Oriented (MOEO) formulated to control the classifier topology and provide feedback support for changing the classifiers' topology and weights based on the extension of Non-dominated Sorting Genetic Algorithm (NSGA-II). It is designated as the Random Subset based Parents Selection (RSPS-NSGA-II) to handle neurons and accuracy. Evaluation metrics from two perspectives classification and Multiobjective optimization. The experimental design based on two subsets of the IFN/ENIT database. The first one consists of 10 classes (C10) and 22 classes (C22). The features were tested with Support Vector Machine (SVM) and Extreme Learning Machine (ELM). This work improved due to the SI feature. SI shows a significant result with SVM with 88.53% for C22. RSPS for C10 at k=2 achieved 91% accuracy with fewer neurons than NSGA-II, and for C22 at k=10, accuracy has been increased 81% compared to NSGA-II 78%. Future work may consider introducing more features to the system, applying them to other languages, and integrating it with sequence learning for more accuracy

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

Feature Extraction Methods for Character Recognition

Not Include