Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking

Hidden Markov models (HMMs) are now widely used for off-line handwriting recognition in many languages and, in particular, in Arabic. As in speech recognition, they are usually built from shared, embedded HMMs at the symbol level, in which state-conditional probability density functions are modeled with Gaussian mixtures. In contrast to speech recognition, however, it is unclear which kinds of features should be used and, indeed, very different feature sets are in use today. Among them, we have recently proposed to simply use columns of raw, binary image pixels, which are directly fed into embedded Bernoulli (mixture) HMMs, that is, embedded HMMs in which the emission probabilities are modeled with Bernoulli mixtures. The idea is to bypass feature extraction and ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. In this chapter, we review this idea along with some extensions that are currently providing state-of-the-art results on Arabic handwritten word recognition.

A system for writer identification based on Arabic handwritten words was built. First a database of words was gathered and used as a test base. Then, features vectors were extracted from writers' word images. Prior to feature extraction, normalization operations were applied to a word or text line. In this research, we studied the feature extraction and recognition operations on Arabic text, on the identification rate of writers. Since there is no well known database containing Arabic handwritten words for researchers to test, we built a new database of off-line Arabic handwriting text to be used for writer identification research. The proposed database is meant to provide training and testing sets for Arabic writer identification research. Arabic handwritten words were collected from 100 writers. We evaluated the performance of edge-based directional probability distributions as features and other features in Arabic writer identification.

Background: The growing use of smart hand-held devices in the daily lives of the people urges for the requirement of online handwritten text recognition. Online handwritten text recognition refers to the identification of the handwritten text at the very moment it is written on a digitizing tablet using some pen-like stylus. Several techniques are available for online handwritten text recognition in English, Arabic, Latin, Chinese, Japanese, and Korean scripts. However, limited research is available for Indic scripts. Objective: This article presents a novel approach for online handwritten numeral and character (simple and compound) recognition of three popular Indic scripts - Devanagari, Bengali and Tamil. Methods: The proposed work employs the Zone wise Slopes of Dominant Points (ZSDP) method for feature extraction from the individual characters. Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifiers are used for recognition process. Recognition efficiency is improved by combining the probabilistic outcomes of the SVM and HMM classifiers using Dempster-Shafer theory. The system is trained using separate as well as combined dataset of numerals, simple and compound characters. Results: The performance of the present system is evaluated using large self-generated datasets as well as public datasets. Results obtained from the present work demonstrate that the proposed system outperforms the existing works in this regard. Conclusion: This work will be helpful to carry out researches on online recognition of handwritten character in other Indic scripts as well as recognition of isolated words in various Indic scripts including the scripts used in the present work.

Arabic handwritten text recognition has not received the same attention as that directed towards Latin script-based languages. In this paper, we present our efforts to develop a comprehensive Arabic Handwritten Text database (AHTD). At this stage, the database will consist of text written by 1000 writers from different countries. Currently, it has data from over 300 writers. It is composed of an images database containing images of the written text at various resolutions, and a ground truth database that contains meta-data describing the written text at the page, paragraph, and line levels. Tools to extract paragraphs from pages, segment paragraphs into lines have also been developed. Segmentation of lines into words will follow. The database will be made freely available to researchers world-wide. It is hoped that the AHTD database will stir research efforts in various handwritten-related problems such as text recognition, and writer identification and verification.

The objective of this research paper is to propose a novel technique for Arabic text extraction and recognition which is a part of research work aimed at developing a system for moving Arabic video text extraction for efficient content based indexing and searching. Numerous techniques were proposed in the past for text extraction but very few of them focus on Arabic text. All the earlier proposed implementations are not successful in attaining 100 % accuracy in text extraction and recognition process. The proposed technique is new and is based on thinning the given sample image containing Arabic text and splitting the resulting image horizontally (X-axis direction) from right to left in equal intervals. Compare each part of the image for equal number of white pixels to those of samples in the dataset. Upon matching, with the help of index value the corresponding character is stored in an array. This process is repeated by varying the splitting interval until all the characters in the sample image are recognized. To our knowledge, our research is the primary to address the above problem and propose a solution with increased retrieval accuracy and reduced computation time for Arabic text extraction and recognition.

Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) are technologies that enable text recognition. The difference between OCR and HTR is one designed specifically for digital text and one designed for handwritten text. There are already various implementations of OCR and HTR online. However, such systems do not guarantee the systems are in premises. To solve this problem, the OCR and HTR system must be built from the scratch. The purpose of this research is to improve the recognition by separating the text whether it is a handwritten or a printed text, which will later be forwarded into the appropriate recognition system. An application program interface (API) was also created in order to finalize the classification system into real world usage. In this research, the classification system being developed using convolutional neural network (CNN) method. To be able to reach the highest accuracy of the classification system, the experimentation and improvement about hyperparameters, dataset format, data augmentation and analysis on 3 CNN architectures were conducted. In the end of this research, there are 2 architectures in a tight competition, one is VGG-16 with 90.63% accuracy and one is AlexNet with 90.17% accuracy on ideal data testing. However, AlexNet is chosen as the winner after the testing with real data.

Offline handwriting recognition of free-flowing Arabic text is a challenging task due to the plethora of factors that contribute to the variability in the data. In this paper, we address some of these sources of variability, and present experimental results on a large corpus of handwritten documents. Specific techniques such as the application of context-dependent Hidden Markov Models (HMMs) for the cursive Arabic script, unsupervised adaptation to account for the stylistic variations across scribes, and image pre-processing to remove ruled-lines are explored. In particular, we proposed a novel integration of structural features in the HMM framework which exclusively results in a 9% relative improvement in performance. Overall, we demonstrate a relative reduction of 17% in word error rate over our baseline Arabic handwriting recognition system.

In this paper, we propose a novel approach for online handwriting recognition (HR) based on hidden Markov model (HMM). In a conventional HMM-based HR system, the input test sample is recognized by first measuring the log-likelihood score from each class-specific HMM, and then the class with the highest score is assigned as the recognized class. It is observed that, for a given test sample, the difference in log-likelihood scores of top-2 outputs (classes) is often less for faithful classification. The problem intensifies for those scripts that have a large set of similar shape characters such as the Indic script. To address this problem, first, we analyze the HMM states corresponding to the top-2 classes and identify a subset of states that most discriminate the two classes. Afterwards, the final recognition among the two classes is carried out by comparing the log-likelihood scores of these chosen states. Since the proposed methodology focuses only on the most discriminative states of the two classes, therefore it enhances the classification confidence as well as overall recognition accuracy with least added complexity. The proposal is demonstrated for character and limited vocabulary word recognition tasks and evaluated on the locally collected Assamese character and word databases. The experimental results are promising over the conventional HMM-based HR system.

Arabic handwritten text recognition is a complex and challenging research domain. This study proposes an offline Arabic handwritten word recognition system based on transfer learning. The system exploits four pre-trained convolutional neural network (CNN) architectures, namely VGG16, ResNet50, AlexNet, and InceptionV3. In addition, a specialized image recognition model derived from the ImageNet dataset is incorporated. A combination strategy is designed to combine transfer learning with specific fine-tuning techniques, aiming to improve recognition accuracy. The study is conducted on the IFN/ENIT dataset, which includes images of Tunisian City and village names. The results show that the proposed system achieves a recognition accuracy of 94.73%, which is significantly higher than the accuracy rates achieved by previous approaches. These results suggest that the proposed system is a promising approach for Arabic handwritten text recognition.

In today’s digital world, we are committed to digitizing thousands of handwritten transcriptions to preserve their content. Historical Arabic Handwritten Text Recognition (HAHTR) remains a challenge for computer vision systems, due to the many difficulties inherently associated with document image quality and the complexity of Arabic script. In this work, we address the problem of recognizing historical Arabic documents that adapts to different writing styles and degrees of legibility. We developed a system that is able to recognize a whole page of a historical Arabic handwritten text in two consecutive steps comprising text line detection and recognition. The proposed approach performs detection using bounding boxes followed by a neural network-based model for character-level text recognition. However, the lack of data hinders the mass digitization of Arabic historical documents. Therefore, we provide a new and freely available dataset, focusing on diverse handwriting styles to facilitate a strong generalization of the trained model. This dataset will significantly benefits researchers and practitioners by accelerating progress in the field of HAHTR. Extensive experimental work demonstrates that the recognition models are effective when trained with different sources of data, and having different writing styles does not penalize the model’s ability to generalize but rather enhances it. Additionally, we define and develop a new metric to evaluate model robustness against character misclassification, particularly for characters with similar patterns. The experiments conducted demonstrated that the proposed HAHTR pipeline is accurate and highly generalizable, as well as the validity of bounding box methods for detecting text lines. The training approach with different data sources enabled us to surpass the state-of-the-art results with 5.7% of Character Error Rate (CER) on the KHATT database.

This paper presents the use of Hidden Markov Models (HMM) to classifying Arabic words that have been represented by moments into a set of predefined classes. The proposed scheme results in enhanced performance because it deals effectively with errors that commonly originate during the feature extraction stage and it accounts for variations due to the individual handwriting performance of different data sets. To overcome the variations in images, each is represented by rotation and scale invariant feature vector known as Pseudo Zernike Moments (PZM). To demonstrate its effectiveness, the proposed classification scheme has been employed in the context of different sets of handwritten Arabic words. The classification results demonstrate that the proposed approach can classify handwritten Arabic word with a error rate less than 10%.

Open-vocabulary recognition of machine-printed Arabic text using hidden Markov models

Recognition of Bangla handwritten text is difficult due to its complex nature of having modifiers and headlines features. This paper presents a comparative study of different features namely LGH (Local Gradient of Histogram), PHOG (Pyramid Histogram of Oriented Gradient), GABOR, G-PHOG (Combined GABOR and PHOG) and profile feature by Marti-Bunke when applied in middle zone recognition of Bangla words using Hidden Markov Model (HMM) based framework. For this purpose, a zone segmentation method is applied to extract the busy (middle) zones of handwritten words and features are extracted from the middle zone. The system has been tested on a sufficiently large and variation-rich dataset consisting of 11,253 training and 3,856 testing data. From the experiment, it has been noted that PHOG feature outperforms other features in middle zone recognition. Since PHOG feature outperform others, we use this feature for full word recognition, For this purpose initially upper and lower zone components are recognized by PHOG features and SVM classifier. Finally, the zone-wise results are combined by the context information of the corresponding components in each zone to obtain the word level recognition.

Research in Arabic handwritten recognition has been of growing interest in the last few decades. This is mainly due to its broad spectrum of applications in different fields such as bank check processing, form data entry, postal mail sorting, automatic processing of old manuscripts, etc. In the literature, numerous techniques have been proposed for feature extraction and applied to various types of images. This work provides a comprehensive review of these methods for off-line handwritten Arabic text recognition. It also presents recognition rates and descriptions of the databases used for the discussed approaches. This paper includes background on the field, discussion of feature extraction methods, and future research directions.

Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things (IoT) systems, much research has been done in using machine learning methods to recognize IoT sensors data. This is particularly the case for optical character recognition of handwritten scripts. Recognizing text in images has several useful applications, including content-based image retrieval, searching and document archiving. The Arabic language is one of the mostly used tongues in the world. However, Arabic text recognition in imagery is still very much in the nascent stage, especially handwritten text. This is mainly due to the language complexities, different writing styles, variations in the shape of characters, diacritics, and connected nature of Arabic text. In this paper, two deep learning models were proposed. The first model was based on a sequence-to-sequence recognition, while the second model was based on a fully convolution network. To measure the performance of these models, a new dataset, called QTID (Quran Text Image Dataset) was devised. This is the first Arabic dataset that includes Arabic diacritics. It consists of 309,720 different 192 × 64 annotated Arabic word images, which comprise 2,494,428 characters in total taken from the Holy Quran. The annotated images in the dataset were randomly divided into 90%, 5%, and 5% sets for training, validation, and testing purposes, respectively. Both models were set up to recognize the Arabic Othmani font in the QTID. Experimental results show that the proposed methods achieve state-of-the-art outcomes. Furthermore, the proposed models surpass expectations in terms of character recognition rate, F1-score, average precision, and recall values. They are superior to the best Arabic text recognition engines like Tesseract and ABBYY FineReader.