Arabic Font Recognition Using Decision Trees Built From Common Words

We present an algorithm for a priori Arabic optical Font Recognition (AFR). The basic idea is to recognize fonts of some common Arabic words. Once these fonts are known, they can be generalized to lines, paragraphs, or neighbor non-common words since these components of a textual material almost have the same font. A decision tree is our approach to recognize Arabic fonts. A set of 48 features is used to learn the tree. These features include horizontal projections, Walsh coefficients, invariant moments, and geometrical attributes. A set of 36 fonts is investigated. The overall success rate is 90.8%. Some fonts show 100% success rate. The average time required to recognize the word font is approximately 0.30 seconds

Analysis and Modular Approach for Text Extraction from Scientific Figures on Limited Data

Scientific figures are widely used as compact, comprehensible representations of important information. The re-usability of these figures is however limited, as one can rarely search directly for them, since they are mostly indexing by their surrounding text (e. g., publication or website) which often does not contain the full-message of the figure. In this thesis, the focus is on making the content of scientific figures accessible by extracting the text from these figures. A modular pipeline for unsupervised text extraction from scientific figures, based on a thorough analysis of the literature, was built to address the problem. This modular pipeline was used to build several unsupervised approaches, to evaluate different methods from the literature and new methods and method combinations. Some supervised approaches were built as well for comparison. One challenge, while evaluating the approaches, was the lack of annotated data, which especially needed to be considered when building the supervised approach. Three existing datasets were used for evaluation as well as two datasets of 241 scientific figures which were manually created and annotated. Additionally, two existing datasets for text extraction from other types of images were used for pretraining the supervised approach. Several experiments showed the superiority of the unsupervised pipeline over common Optical Character Recognition engines and identified the best unsupervised approach. This unsupervised approach was compared with the best supervised approach, which, despite of the limited amount of training data available, clearly outperformed the unsupervised approach.Infografiken sind ein viel verwendetes Medium zur kompakten Darstellung von Kernaussagen. Die Nachnutzbarkeit dieser Abbildungen ist jedoch häufig limitiert, da sie schlecht auffindbar sind, da sie meist über die umschließenden Medien, wie beispielsweise Publikationen oder Webseiten, und nicht über ihren Inhalt indexiert sind. Der Fokus dieser Arbeit liegt auf der Extraktion der textuellen Inhalte aus Infografiken, um deren Inhalt zu erschließen. Ausgehend von einer umfangreichen Analyse verwandter Arbeiten, wurde ein generalisierender, modularer Ansatz für die unüberwachte Textextraktion aus wissenschaftlichen Abbildungen entwickelt. Mit diesem modularen Ansatz wurden mehrere unüberwachte Ansätze und daneben auch noch einige überwachte Ansätze umgesetzt, um diverse Methoden aus der Literatur sowie neue und bisher noch nicht genutzte Methoden zu vergleichen. Eine Herausforderung bei der Evaluation war die geringe Menge an annotierten Abbildungen, was insbesondere beim überwachten Ansatz Methoden berücksichtigt werden musste. Für die Evaluation wurden drei existierende Datensätze verwendet und zudem wurden zusätzlich zwei Datensätze mit insgesamt 241 Infografiken erstellt und mit den nötigen Informationen annotiert, sodass insgesamt 5 Datensätze für die Evaluation verwendet werden konnten. Für das Pre-Training des überwachten Ansatzes wurden zudem zwei Datensätze aus verwandten Textextraktionsbereichen verwendet. In verschiedenen Experimenten wird gezeigt, dass der unüberwachte Ansatz besser funktioniert als klassische Texterkennungsverfahren und es wird aus den verschiedenen unüberwachten Ansätzen der beste ermittelt. Dieser unüberwachte Ansatz wird mit dem überwachten Ansatz verglichen, der trotz begrenzter Trainingsdaten die besten Ergebnisse liefert

CHARACTER-LEVEL INTERACTIONS IN MULTIMODAL COMPUTER-ASSISTED TRANSCRIPTION OF TEXT IMAGES

HTR systems don't achieve acceptable results in unconstrained applications. Therefore, it is convenient to use a system that allows the user to cooperate in the most confortable way with the system to generate a correct transcription. In this paper, multimodal interaction at character-level is studied.Martín-Albo Simón, D. (2011). CHARACTER-LEVEL INTERACTIONS IN MULTIMODAL COMPUTER-ASSISTED TRANSCRIPTION OF TEXT IMAGES. http://hdl.handle.net/10251/11313Archivo delegad

Knowledge-based document retrieval with application to TEXPROS

Document retrieval in an information system is most often accomplished through keyword search. The common technique behind keyword search is indexing. The major drawback of such a search technique is its lack of effectiveness and accuracy. It is very common in a typical keyword search over the Internet to identify hundreds or even thousands of records as the potentially desired records. However, often few of them are relevant to users\u27 interests. This dissertation presents knowledge-based document retrieval architecture with application to TEXPROS. The architecture is based on a dual document model that consists of a document type hierarchy and, a folder organization. Using the knowledge collected during document filing, the search space can be narrowed down significantly. Combining the classical text-based retrieval methods with the knowledge-based retrieval can improve tremendously both search efficiency and effectiveness. With the proposed predicate-based query language, users can more precisely and accurately specify the search criteria and their knowledge about the documents to be retrieved. To assist users formulate a query, a guided search is presented as part of an intelligent user interface. Supported by an intelligent question generator, an inference engine, a question base, and a predicate-based query composer, the guided search collects the most important information known to the user to retrieve the documents that satisfy users\u27 particular interests. A knowledge-based query processing and search engine is presented as the core component in this architecture. Algorithms are developed for the search engine to effectively and efficiently retrieve the documents that match the query. Cache is introduced to speed up the process of query refinement. Theoretical proof and performance analysis are performed to prove the efficiency and effectiveness of this knowledge-based document retrieval approach

Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network

This study was supported by the China University of Petroleum-Beijing and Fundamental Research Funds for Central Universities under Grant no. 2462020YJRC001.Peer reviewedPublisher PD

A large vocabulary online handwriting recognition system for Turkish

Handwriting recognition in general and online handwriting recognition in particular has been an active research area for several decades. Most of the research have been focused on English and recently on other scripts like Arabic and Chinese. There is a lack of research on recognition in Turkish text and this work primarily fills that gap with a state-of-the-art recognizer for the first time. It contains design and implementation details of a complete recognition system for recognition of Turkish isolated words. Based on the Hidden Markov Models, the system comprises pre-processing, feature extraction, optical modeling and language modeling modules. It considers the recognition of unconstrained handwriting with a limited vocabulary size first and then evolves to a large vocabulary system. Turkish script has many similarities with other Latin scripts, like English, which makes it possible to adapt strategies that work for them. However, there are some other issues which are particular to Turkish that should be taken into consideration separately. Two of the challenging issues in recognition of Turkish text are determined as delayed strokes which introduce an extra source of variation in the sequence order of the handwritten input and high Out-of-Vocabulary (OOV) rate of Turkish when words are used as vocabulary units in the decoding process. This work examines the problems and alternative solutions at depth and proposes suitable solutions for Turkish script particularly. In delayed stroke handling, first a clear definition of the delayed strokes is developed and then using that definition some alternative handling methods are evaluated extensively on the UNIPEN and Turkish datasets. The best results are obtained by removing all delayed strokes, with up to 2.13% and 2.03% points recognition accuracy increases, over the respective baselines of English and Turkish. The overall system performances are assessed as 86.1% with a 1,000-word lexicon and 83.0% with a 3,500-word lexicon on the UNIPEN dataset and 91.7% on the Turkish dataset. Alternative decoding vocabularies are designed with grammatical sub-lexical units in order to solve the problem of high OOV rate. Additionally, statistical bi-gram and tri-gram language models are applied during the decoding process. The best performance, 67.9% is obtained by the large stem-ending vocabulary that is expanded with a bi-gram model on the Turkish dataset. This result is superior to the accuracy of the word-based vocabulary (63.8%) with the same coverage of 95% on the BOUN Web Corpus

DocMIR: An automatic document-based indexing system for meeting retrieval

This paper describes the DocMIR system which captures, analyzes and indexes automatically meetings, conferences, lectures, etc. by taking advantage of the documents projected (e.g. slideshows, budget tables, figures, etc.) during the events. For instance, the system can automatically apply the above-mentioned procedures to a lecture and automatically index the event according to the presented slides and their contents. For indexing, the system requires neither specific software installed on the presenter's computer nor any conscious intervention of the speaker throughout the presentation. The only material required by the system is the electronic presentation file of the speaker. Even if not provided, the system would temporally segment the presentation and offer a simple storyboard-like browsing interface. The system runs on several capture boxes connected to cameras and microphones that records events, synchronously. Once the recording is over, indexing is automatically performed by analyzing the content of the captured video containing projected documents and detects the scene changes, identifies the documents, computes their duration and extracts their textual content. Each of the captured images is identified from a repository containing all original electronic documents, captured audio-visual data and metadata created during post-production. The identification is based on documents' signatures, which hierarchically structure features from both layout structure and color distributions of the document images. Video segments are finally enriched with textual content of the identified original documents, which further facilitate the query and retrieval without using OCR. The signature-based indexing method proposed in this article is robust and works with low-resolution images and can be applied to several other applications including real-time document recognition, multimedia IR and augmented reality system