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

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

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

Adaptive Algorithms for Automated Processing of Document Images

Large scale document digitization projects continue to motivate interesting document understanding technologies such as script and language identification, page classification, segmentation and enhancement. Typically, however, solutions are still limited to narrow domains or regular formats such as books, forms, articles or letters and operate best on clean documents scanned in a controlled environment. More general collections of heterogeneous documents challenge the basic assumptions of state-of-the-art technology regarding quality, script, content and layout. Our work explores the use of adaptive algorithms for the automated analysis of noisy and complex document collections. We first propose, implement and evaluate an adaptive clutter detection and removal technique for complex binary documents. Our distance transform based technique aims to remove irregular and independent unwanted foreground content while leaving text content untouched. The novelty of this approach is in its determination of best approximation to clutter-content boundary with text like structures. Second, we describe a page segmentation technique called Voronoi++ for complex layouts which builds upon the state-of-the-art method proposed by Kise [Kise1999]. Our approach does not assume structured text zones and is designed to handle multi-lingual text in both handwritten and printed form. Voronoi++ is a dynamically adaptive and contextually aware approach that considers components' separation features combined with Docstrum [O'Gorman1993] based angular and neighborhood features to form provisional zone hypotheses. These provisional zones are then verified based on the context built from local separation and high-level content features. Finally, our research proposes a generic model to segment and to recognize characters for any complex syllabic or non-syllabic script, using font-models. This concept is based on the fact that font files contain all the information necessary to render text and thus a model for how to decompose them. Instead of script-specific routines, this work is a step towards a generic character and recognition scheme for both Latin and non-Latin scripts

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Human Reading Based Strategies for off-line Arabic Word Recognition

International audienceThis paper summarizes some techniques proposed for off-line Arabic word recognition. The point of view developed here concerns the human reading favoring an interactive mechanism between global memorization and local checking making easier the recognition of complex scripts as Arabic. According to this consideration, some specific papers are analyzed and their strategies commente

End-Shape Analysis for Automatic Segmentation of Arabic Handwritten Texts

Word segmentation is an important task for many methods that are related to document understanding especially word spotting and word recognition. Several approaches of word segmentation have been proposed for Latin-based languages while a few of them have been introduced for Arabic texts. The fact that Arabic writing is cursive by nature and unconstrained with no clear boundaries between the words makes the processing of Arabic handwritten text a more challenging problem. In this thesis, the design and implementation of an End-Shape Letter (ESL) based segmentation system for Arabic handwritten text is presented. This incorporates four novel aspects: (i) removal of secondary components, (ii) baseline estimation, (iii) ESL recognition, and (iv) the creation of a new off-line CENPARMI ESL database. Arabic texts include small connected components, also called secondary components. Removing these components can improve the performance of several systems such as baseline estimation. Thus, a robust method to remove secondary components that takes into consideration the challenges in the Arabic handwriting is introduced. The methods reconstruct the image based on some criteria. The results of this method were subsequently compared with those of two other methods that used the same database. The results show that the proposed method is effective. Baseline estimation is a challenging task for Arabic texts since it includes ligature, overlapping, and secondary components. Therefore, we propose a learning-based approach that addresses these challenges. Our method analyzes the image and extracts baseline dependent features. Then, the baseline is estimated using a classifier. Algorithms dealing with text segmentation usually analyze the gaps between connected components. These algorithms are based on metric calculation, finding threshold, and/or gap classification. We use two well-known metrics: bounding box and convex hull to test metric-based method on Arabic handwritten texts, and to include this technique in our approach. To determine the threshold, an unsupervised learning approach, known as the Gaussian Mixture Model, is used. Our ESL-based segmentation approach extracts the final letter of a word using rule-based technique and recognizes these letters using the implemented ESL classifier. To demonstrate the benefit of text segmentation, a holistic word spotting system is implemented. For this system, a word recognition system is implemented. A series of experiments with different sets of features are conducted. The system shows promising results