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Extracting text from scanned Arabic books: a large-scale benchmark dataset and a fine-tuned Faster-R-CNN model

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Abstract

Datasets of documents in Arabic are urgently needed to promote computer vision and natural language processing research that addresses the specifics of the language. Unfortunately, publicly available Arabic datasets are limited in size and restricted to certain document domains. This paper presents the release of BE-Arabic-9K, a dataset of more than 9000 high-quality scanned images from over 700 Arabic books. Among these, 1500 images have been manually segmented into regions and labeled by their functionality. BE-Arabic-9K includes book pages with a wide variety of complex layouts and page contents, making it suitable for various document layout analysis and text recognition research tasks. The paper also presents a page layout segmentation and text extraction baseline model based on fine-tuned Faster R-CNN structure (FFRA). This baseline model yields cross-validation results with an average accuracy of 99.4% and F1 score of 99.1% for text versus non-text block classification on 1500 annotated images of BE-Arabic-9K. These results are remarkably better than those of the state-of-the-art Arabic book page segmentation system ECDP. FFRA also outperforms three other prior systems when tested on a competition benchmark dataset, making it an outstanding baseline model to challenge.

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Notes

  1. https://github.com/wdqin/BE-Arabic-9K.

  2. https://github.com/wdqin/BE-Arabic-9K.

  3. https://github.com/wdqin/BE-Arabic-9K.

  4. https://github.com/wdqin/BE-Arabic-9K

  5. https://www.mturk.com/mturk/welcome.

  6. https://www.crowdflower.com/ (re-branded as ’Figure Eight’ starting 2018

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Acknowledgements

The authors would like to thank the library staff at the Mugar Library at Boston University, the Rotch Library at MIT, and the Widener and Fine Arts libraries at Harvard University for facilitating the collection process of our dataset BE-Arabic-9K. The authors thank the National Science Foundation (1838193) and the Hariri Institute for Computing at Boston University for partial support of this work.

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Authors and Affiliations

  1. Computers and Systems Department, Electronics Research Institute, Cairo, Egypt

    Randa Elanwar

  2. Boston University, Boston, USA

    Wenda Qin & Margrit Betke

  3. Department of Computer Science, Boston University, Boston, USA

    Derry Wijaya

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  1. Randa Elanwar
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Correspondence to Randa Elanwar.

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Appendix: Documents images datasets

Appendix: Documents images datasets

1.1 Collection and annotation

Large size annotated datasets are one crucial resource needed for supervised machine learning. Researchers often spend a considerable amount of time annotating their self-collected datasets because they cannot find a publicly available dataset that match their research need. This may result in them ending up conducting their research on limited size datasets.

Unless special characteristics are required for a research dataset, the data collection phase is not as challenging or as expensive as data annotation. For example, The internet archive has billions of unlabeled images that could be downloaded using web search crawlers, an approach that has been followed before to construct large public computer vision datasets like TinyImage [59] and ImageNet [23]. The annotation phase is what controls the research outcome. Annotations should match the research question, meaning that a single image can have multiple annotations and several levels of details. Accordingly, the annotation process has been always expert-based and problem-oriented, expensive and time consuming.

Dataset collection for document analysis and recognition is one of the most challenging tasks compared to other research areas. One might expect ”transcripts” as the only annotation needed for documents images, however, according to the research problem the required annotations might include much more information like:

  1. 1.

    Segmentation information: locating the text position inside the document image (i.e., bounding box coordinates),

  2. 2.

    Logical labeling: identification of the text logical function (i.e., title, caption, footnote, etc.)

  3. 3.

    the text reading order (specially in multi-columns layouts)

  4. 4.

    Geometrical labeling: classifying the non-text element type (i.e., image, chart, map, logo, math formulae, etc.)

  5. 5.

    Descriptions: the alternative text for image elements, cells functions-and-relations for tables elements.

Logical labeling of a document’s text elements, is one of the most human-intelligence-based tasks that sometimes become controversial especially with unfamiliar layouts or with the absence of appropriate text formatting (e.g., font size and emphasis).

Our previous attempt to provide the first labeled dataset for page segmentation and layout analysis BCE-Arabic V1 was one of a kind [53]. We investigated the importance of having physically analyzed documents (i.e., segmenting regions and identifying their type as text or non-text), and showed that it is no trivial task and has a significant impact on improving the OCR results compared to introducing raw images to the system.

Our study highlighted behind-the-seen efforts of sample annotation and selecting the appropriate metadata set and labeling tools to prepare a dataset of document images. We discussed the tools used by researchers and set comparison to discover the most suitable one for annotating an Arabic documents dataset (Aletheia tool).

The document image annotation standards were finally created after studying the most common labels and metadata hierarchy needed for representing a document content in many research areas and PAGE format (created by the Aletheia tool) ended up being the most comprehensive annotation scheme for such representation.

1.2 Crowdsourcing for datasets annotation

Crowdsourcing has been recently used for constructing different relatively large image, audio and video research datasets through annotation tasks, like segmentation and labeling.

It has proved to be very fast and cheap, compared to the expert-based method. However, this has not yet been commonly used in all research areas.

Crowdsourcing was not only used for computer vision dataset annotation but also for natural language processing (NLP) datasets. Datasets for named entity recognition [38], image transcriptions [50] were annotated using crowdsourcing.

Annotating text corpora, and social media tweets and comments in the form of transcripts, dialects, sentiment analysis and people opinions or orientations were all done through crowdsourcing. Literature about crowdsourcing for annotation of Arabic datasets all lie in this area [3, 21, 26, 29, 62, 64, 65].

However, as far as we know, there is still no attempt for crowdsourcing to annotate scanned documents dataset for the sake of Arabic document image analysis and recognition research, we were the first.

1.3 Amazon mechanical Turk (MTurk)

Researchers used the crowdsourcing services offered by Artificial intelligence companies at very small profit like Amazon Mechanical Turk (MTurk)Footnote 5, and CrowdFlower (CF)Footnote 6. for the purpose of information collection or fast accomplishment of tedious small tasks. Amazon MTurk might be the first and most popular crowdsourcing platform used by researchers.

An MTurk job/HIT The requester divides the entire task to a large number of small jobs (also called human intelligent tasks ’HITs’), that could be done in parallel. Usually the tasks are short time data entry or information extraction, for example answering questions about identifying and/or segmenting an object, or selecting an appropriate label, etc.

An instructions set of how the task should be performed with examples of possible instances and common errors are posted to the workers once they accept to do the job. The requester also specifies the maximum time duration for accomplishing the job, and the monetary reward for the given job.

The requesters can select workers based on specific qualities related to their tasks and the same HIT could also be assigned to multiple workers for quality assurance.

Upon posting the jobs to MTurk, workers try to accept the job, perform the job according to the instructions, and submit it before the specified deadline. Workers might choose long duration jobs with high rewards or a number of short duration jobs with smaller lump-sum.

After the jobs submission, the requesters have a deadline to review them and agree to paying/not-paying the workers individually according the job quality. Some requesters choose to offer bonuses beyond the basic reward to some high quality workers as well.

Rewards could be as less as two cents and could be as high as tens of dollars according to the job difficulty. The rewards do not represent the entire task pricing, as the budget also include the service provider profit (percentage of the rewards and bonuses).

The process is completed by quality assurance procedures and tests to detect spammers and insure agreement between the workers performing the same HIT and also evaluating the annotation accuracy and analyzing errors.

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Elanwar, R., Qin, W., Betke, M. et al. Extracting text from scanned Arabic books: a large-scale benchmark dataset and a fine-tuned Faster-R-CNN model. IJDAR 24, 349–362 (2021). https://doi.org/10.1007/s10032-021-00382-4

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