PSL
Abstract:This work presents an accuracy study of the open source OCR engine, Kraken, on the leading Arabic scholarly journal, al-Abhath. In contrast with other commercially available OCR engines, Kraken is shown to be capable of producing highly accurate Arabic-script OCR. The study also assesses the relative accuracy of typeface-specific and generalized models on the al-Abhath data and provides a microanalysis of the ``error instances'' and the contextual features that may have contributed to OCR misrecognition. Building on this analysis, the paper argues that Arabic-script OCR can be significantly improved through (1) a more systematic approach to training data production, and (2) the development of key technological components, especially multi-language models and improved line segmentation and layout analysis. Cet article pr{\'e}sente une {\'e}tude d'exactitude du moteur ROC open source, Krakan, sur la revue acad{\'e}mique arabe de premier rang, al-Abhath. Contrairement {\`a} d'autres moteurs ROC disponibles sur le march{\'e}, Kraken se r{\'e}v{\`e}le {\^e}tre capable de produire de la ROC extr{\^e}mement exacte de l'{\'e}criture arabe. L'{\'e}tude {\'e}value aussi l'exactitude relative des mod{\`e}les sp{\'e}cifiquement configur{\'e}s {\`a} des polices et celle des mod{\`e}les g{\'e}n{\'e}ralis{\'e}s sur les donn{\'e}es d'al-Abhath et fournit une microanalyse des "occurrences d'erreurs", ainsi qu'une microanalyse des {\'e}l{\'e}ments contextuels qui pourraient avoir contribu{\'e} {\`a} la m{\'e}reconnaissance ROC. S'appuyant sur cette analyse, cet article fait valoir que la ROC de l'{\'e}criture arabe peut {\^e}tre consid{\'e}rablement am{\'e}lior{\'e}e gr{\^a}ce {\`a} (1) une approche plus syst{\'e}matique d'entra{\^i}nement de la production de donn{\'e}es et (2) gr{\^a}ce au d{\'e}veloppement de composants technologiques fondamentaux, notammentl'am{\'e}lioration des mod{\`e}les multilingues, de la segmentation de ligne et de l'analyse de la mise en page.
Abstract:The application of handwritten text recognition to historical works is highly dependant on accurate text line retrieval. A number of systems utilizing a robust baseline detection paradigm have emerged recently but the advancement of layout analysis methods for challenging scripts is held back by the lack of well-established datasets including works in non-Latin scripts. We present a dataset of 400 annotated document images from different domains and time periods. A short elaboration on the particular challenges posed by handwriting in Arabic script for layout analysis and subsequent processing steps is given. Lastly, we propose a method based on a fully convolutional encoder-decoder network to extract arbitrarily shaped text line images from manuscripts.
Abstract:The OpenITI team has achieved Optical Character Recognition (OCR) accuracy rates for classical Arabic-script texts in the high nineties. These numbers are based on our tests of seven different Arabic-script texts of varying quality and typefaces, totaling over 7,000 lines. These accuracy rates not only represent a distinct improvement over the actual accuracy rates of the various proprietary OCR options for classical Arabic-script texts, but, equally important, they are produced using an open-source OCR software, thus enabling us to make this Arabic-script OCR technology freely available to the broader Islamic, Persian, and Arabic Studies communities.