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

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

A high level approach to Arabic sentence recognition

The aim of this work is to develop sentence recognition system inspired by the human reading process. Cognitive studies observed that the human tended to read a word as a whole at a time. He considers the global word shapes and uses contextual knowledge to infer and discriminate a word among other possible words. The sentence recognition system is a fully integrated system; a word level recogniser (baseline system) integrated with linguistic knowledge post-processing module. The presented baseline system is holistic word-based recognition approach characterised as probabilistic ranked task. The output of the system is multiple recognition hypotheses (N-best word lattice). The basic unit is the word rather than the character; it does not rely on any segmentation or require baseline detection. The considered linguistic knowledge to re-rank the output of the existing baseline system is the standard n-gram Statistical Language Models (SLMs). The candidates are re-ranked through exploiting phrase perplexity score. The system is an OCR system that depends on HMM models utilizing the HTK Toolkit. The baseline system supported by global transformation features extracted from binary word images. The adopted features' extraction technique is the block-based Discrete Cosine Transform (DCT) applied to the whole word image. Feature vectors extracted using block-based DCT with non-overlapping sub-block of size 8x8 pixels. The applied HMMs to the task are mono-model discrete one-dimensional HMMs (Bakis Model). A balanced actual scanned and synthetic database of word-image has been constructed to ensure an even distribution of word samples. The Arabic words are typewritten in five fonts having a size 14 points in a plain style. The statistical language models and lexicon words are extracted from The Holy Qur‟an. The systems are applied on word images with no overlap between the training and testing datasets. The actual scanned database is used to evaluate the word recogniser. The synthetic database is a large amount of data acquired for a reliable training of sentence recognition systems. This word recogniser evaluated in mono-font and multi-font contexts. The two types of word recogniser have been used to achieve a final recognition accuracy of99.30% and 73.47% in mono-font and multi-font, respectively. The achieved average accuracy by the sentence recogniser is 67.24% improved to 78.35% on average when using 5-gram post-processing. The complexity and accuracy of the post-processing module are evaluated and found that 4-gram is more suitable than 5-gram; it is much faster at an average improvement of 76.89%

Qualidade dos dados & Machine Learning : uma nova abordagem aos censos populacionais e habitacionais

Mestrado em Gestão de Sistemas de InformaçãoO projeto realizado consiste no processo de recolha e preparação de dados manuscritos em papel, da aplicação do inquérito Censo Populacional e Habitacional a uma população de mais de vinte milhões de pessoas. Este é um tipo de inquérito que se faz à população de um país, tendo como objetivo retirar conclusões a nível geográfico tanto da população, como das suas condições de vida. Os Censos são realizados com alguma frequência, o que permite efetuar comparações e perceber a transformação da sociedade e de um país, ao longo dos anos. Com o objetivo de tornar os mais de vinte milhões de inquéritos manuscritos em informação útil e de qualidade acerca de um país e de uma população foi necessário dividir o trabalho em três fases, a fase recolha de dados e da sua conversão de imagem para um formato digital onde o texto possa ser editável, a fase de limpeza e tratamento dos dados e, por último, a fase de análise e classificação dos mesmos. De acordo com cada fase, foram utilizadas diversas metodologias e tecnologias, como é o caso do OCR (Optical Character Recognition), NLP (Natural Language Processing) e Machine Learning, respetivamente. Estas abordagens permitiram uma melhor, mais rápida e mais fiável análise de resultados.The project undertaken consists on the process of collecting and preparing paper handwritten data obtained from the Population and Housing Census survey applied to a population of over twenty million people. This type of inquiry done to the population of a country has the purpose of drawing up conclusions and insights on the populations' geographical characteristics, as well as their life conditions. These censuses are done on a frequent basis, which allows for continuous comparisons to be done and thus understand the changes occurring in a given society and country throughout time. In order to turn more than twenty million handwritten surveys into useful and quality information about a country and a population, it was necessary to divide the work into three phases. The first stage consisted on the collection of data and its conversion into an image in a digital format, where text can be edited, followed by data cleansing and transformation, and finally, the third stage involved the analysis of the data and its respective classification. In regards to the data analysis, for each sentence there were various methodologies and technologies applied, such as OCR (Optical Character Recognition), NLP (Natural Language Processing) e Machine Learning. This approach led to a better, quicker and more reliable analysis of the data.info:eu-repo/semantics/publishedVersio

Ontology model for zakat hadith knowledge based on causal relationship, semantic relatedness and suggestion extraction

Hadith is the second most important source used by all Muslims. However, semantic ambiguity in the hadith raises issues such as misinterpretation, misunderstanding, and misjudgement of the hadith’s content. How to tackle the semantic ambiguity will be focused on this research (RQ). The Zakat hadith data should be expressed semantically by changing the surface-level semantics to a deeper sense of the intended meaning. This can be achieved using an ontology model covering three main aspects (i.e., semantic relationship extraction, causal relationship representation, and suggestion extraction). This study aims to resolve the semantic ambiguity in hadith, particularly in the Zakat topic by proposing a semantic approach to resolve semantic ambiguity, representing causal relationships in the Zakat ontology model, proposing methods to extract suggestion polarity in hadith, and building the ontology model for Zakat topic. The selection of the Zakat topic is based on the survey findings that respondents still lack knowledge and understanding of the Zakat process. Four hadith book types (i.e., Sahih Bukhari, Sahih Muslim, Sunan Abu Dawud, and Sunan Ibn Majah) that was covering 334 concept words and 247 hadiths were analysed. The Zakat ontology modelling cover three phases which are Preliminary study, source selection and data collection, data pre-processing and analysis, and development and evaluation of ontology models. Domain experts in language, Zakat hadith, and ontology have evaluated the Zakat ontology and identified that 85% of Zakat concept was defined correctly. The Ontology Usability Scale was used to evaluate the final ontology model. An expert in ontology development evaluated the ontology that was developed in Protégé OWL, while 80 respondents evaluated the ontology concepts developed in PHP systems. The evaluation results show that the Zakat ontology has resolved the issue of ambiguity and misunderstanding of the Zakat process in the Zakat hadith. The Zakat ontology model also allows practitioners in Natural language processing (NLP), hadith, and ontology to extract Zakat hadith based on the representation of a reusable formal model, as well as causal relationships and the suggestion polarity of the Zakat hadith