Hybrid model of post-processing techniques for Arabic optical character recognition

Optical character recognition (OCR) is used to extract text contained in an image. One of the stages in OCR is the post-processing and it corrects the errors of OCR output text. The OCR multiple outputs approach consists of three processes: differentiation, alignment, and voting. Existing differentiation techniques suffer from the loss of important features as it uses N-versions of input images. On the other hand, alignment techniques in the literatures are based on approximation while the voting process is not context-aware. These drawbacks lead to a high error rate in OCR. This research proposed three improved techniques of differentiation, alignment, and voting to overcome the identified drawbacks. These techniques were later combined into a hybrid model that can recognize the optical characters in the Arabic language. Each of the proposed technique was separately evaluated against three other relevant existing techniques. The performance measurements used in this study were Word Error Rate (WER), Character Error Rate (CER), and Non-word Error Rate (NWER). Experimental results showed a relative decrease in error rate on all measurements for the evaluated techniques. Similarly, the hybrid model also obtained lower WER, CER, and NWER by 30.35%, 52.42%, and 47.86% respectively when compared to the three relevant existing models. This study contributes to the OCR domain as the proposed hybrid model of post-processing techniques could facilitate the automatic recognition of Arabic text. Hence, it will lead to a better information retrieval

Détection et correction automatique d'entités nommées dans des corpus OCRisés

National audienceCorrection of textual data obtained by optical character recognition (OCR) for reaching editorial quality is an expensive task, as it still involves human intervention. The coverage of statistical models for automated error detection and correction is inherently limited to errors that resort to general language. However, a large amount of errors reside in domain-specific named entities, especially when dealing with data such as patent corpora or legal texts. In this paper, we propose a rule-based architecture for the identification and correction of a wide range of named entities (proper names not included). We show that our architecture achieves a good recall and an excellent correction accuracy on error types that are difficult to adress with statistical approaches.La correction de données textuelles obtenues par reconnaissance optique de caractères (OCR) pour at- teindre une qualité éditoriale reste aujourd'hui une tâche coûteuse, car elle implique toujours une intervention humaine. La détection et la correction automatiques d'erreurs à l'aide de modèles statistiques ne permettent de traiter de façon utile que les erreurs relevant de la langue générale. C'est pourtant dans certaines entités nommées que résident les erreurs les plus nombreuses, surtout dans des données telles que des corpus de brevets ou des textes juridiques. Dans cet article, nous proposons une architecture d'identification et de correction par règles d'un large éventail d'entités nommées (non compris les noms propres). Nous montrons que notre architecture permet d'atteindre un bon rappel et une excellente précision en correction, ce qui permet de traiter des fautes difficiles à traiter par les approches statistiques usuelles

Computational Analysis of Historical Documents: An Application to Italian War Bulletins in World War I and II

World War (WW) I and II represent crucial landmarks in the history on mankind: They have affected the destiny of whole generations and their consequences are still alive throughout Europe. In this paper we present an ongoing project to carry out a computational analysis of Italian war bulletins in WWI and WWII, by applying state-of-the-art tools for NLP and Information Extraction. The annotated texts and extracted information will be explored with a dedicated Web interface, allowing for multidimensional access and exploration of historical events through space and time