6 research outputs found

    DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning

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    This paper presents a novel iterative deep learning framework and apply it for document enhancement and binarization. Unlike the traditional methods which predict the binary label of each pixel on the input image, we train the neural network to learn the degradations in document images and produce the uniform images of the degraded input images, which allows the network to refine the output iteratively. Two different iterative methods have been studied in this paper: recurrent refinement (RR) which uses the same trained neural network in each iteration for document enhancement and stacked refinement (SR) which uses a stack of different neural networks for iterative output refinement. Given the learned uniform and enhanced image, the binarization map can be easy to obtain by a global or local threshold. The experimental results on several public benchmark data sets show that our proposed methods provide a new clean version of the degraded image which is suitable for visualization and promising results of binarization using the global Otsu's threshold based on the enhanced images learned iteratively by the neural network.Comment: Accepted by Pattern Recognitio

    Determination of fibers volume fraction in layered composite materials by optical methods

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    РассматриваСтся Π·Π°Π΄Π°Ρ‡Π° опрСдСлСния ΠΎΠ±ΡŠΡ‘ΠΌΠ½ΠΎΠ³ΠΎ содСрТания Π°Ρ€ΠΌΠΈΡ€ΡƒΡŽΡ‰Π΅Π³ΠΎ Π²ΠΎΠ»ΠΎΠΊΠ½Π° Π² нитях слоистых ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡ‚ΠΎΠ² с Ρ‚ΠΊΠ°Π½Π΅Π²Ρ‹ΠΌΠΈ заполнитСлями. Π’ качСствС источника ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΎ структурС ΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Π° ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‚ΡΡ Ρ†ΠΈΡ„Ρ€ΠΎΠ²Ρ‹Π΅ микроизобраТСния ΡˆΠ»ΠΈΡ„ΠΎΠ²Π°Π½Π½ΠΎΠΉ повСрхности ΠΏΠΎΠΏΠ΅Ρ€Π΅Ρ‡Π½Ρ‹Ρ… сСчСний ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡ‚ΠΎΠ². ΠžΠ±ΡΡƒΠΆΠ΄Π°ΡŽΡ‚ΡΡ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ ΠΈ особСнности Π°Π½Π°Π»ΠΈΠ·Π° растровых микроскопичСских снимков Π³Π΅Ρ‚Π΅Ρ€ΠΎΠ³Π΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Π°, связанныС с ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΡΡ€ΠΊΠΎΡΡ‚ΡŒΡŽ пиксСлСй ΠΈ Ρ€Π°Π·ΠΌΡ‹Ρ‚ΠΎΡΡ‚ΡŒΡŽ Π³Ρ€Π°Π½ΠΈΡ† Β«Π²ΠΎΠ»ΠΎΠΊΠ½ΠΎ-ΡΠ²ΡΠ·ΡƒΡŽΡ‰Π΅Π΅Β». Π’ цСлях сокращСния трудоёмкости ΠΈ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΠΈ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ прСдлагаСтся ΠΈ строится ΡΠΏΠ΅Ρ†ΠΈΠ°Π»ΡŒΠ½Ρ‹ΠΉ автоэнкодСр. ИзлоТСниС сопровоТдаСтся сквозным дСмонстрационным ΠΏΡ€ΠΈΠΌΠ΅Ρ€ΠΎΠΌ исслСдования структуры Ρ‚ΠΈΠΏΠΎΠ²ΠΎΠ³ΠΎ конструкционного углСпластика. Показано сущСствСнноС ускорСниС процСсса ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ с использованиСм свёрточного автоэнкодСра ΠΈ Ρ…ΠΎΡ€ΠΎΡˆΠ΅Π΅ согласованиС Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² с Ρ‚Ρ‰Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹ΠΌ Ρ€ΡƒΡ‡Π½Ρ‹ΠΌ Π°Π½Π°Π»ΠΈΠ·ΠΎΠΌ

    BiNet:Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks

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    Handwritten document-image binarization is a semantic segmentation process to differentiate ink pixels from background pixels. It is one of the essential steps towards character recognition, writer identification, and script-style evolution analysis. The binarization task itself is challenging due to the vast diversity of writing styles, inks, and paper materials. It is even more difficult for historical manuscripts due to the aging and degradation of the documents over time. One of such manuscripts is the Dead Sea Scrolls (DSS) image collection, which poses extreme challenges for the existing binarization techniques. This article proposes a new binarization technique for the DSS images using the deep encoder-decoder networks. Although the artificial neural network proposed here is primarily designed to binarize the DSS images, it can be trained on different manuscript collections as well. Additionally, the use of transfer learning makes the network already utilizable for a wide range of handwritten documents, making it a unique multi-purpose tool for binarization. Qualitative results and several quantitative comparisons using both historical manuscripts and datasets from handwritten document image binarization competition (H-DIBCO and DIBCO) exhibit the robustness and the effectiveness of the system. The best performing network architecture proposed here is a variant of the U-Net encoder-decoders.Comment: 26 pages, 15 figures, 11 table

    Image Enhancement for Scanned Historical Documents in the Presence of Multiple Degradations

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    Historical documents are treasured sources of information but typically suffer from problems with quality and degradation. Scanned images of historical documents suffer from difficulties due to paper quality and poor image capture, producing images with low contrast, smeared ink, bleed-through and uneven illumination. This PhD thesis proposes a novel adaptative histogram matching method to remove these artefacts from scanned images of historical documents. The adaptive histogram matching is modelled to create an ideal histogram by dividing the histogram using its Otsu level and applying Gaussian distributions to each segment with iterative output refinement applied to individual images. The pre-processing techniques of contrast stretching, wiener filtering, and bilateral filtering are used before the proposed adaptive histogram matching approach to maximise the dynamic range and reduce noise. The goal is to better represent document images and improve readability and the source images for Optical Character Recognition (OCR). Unlike other enhancement methods designed for single artefacts, the proposed method enhances multiple (low-contrast, smeared-ink, bleed-through and uneven illumination). In addition to developing an algorithm for historical document enhancement, the research also contributes a new dataset of scanned historical newspapers (an annotated subset of the Europeana Newspaper - ENP – dataset) where the enhancement technique is tested, which can also be used for further research. Experimental results show that the proposed method significantly reduces background noise and improves image quality on multiple artefacts compared to other enhancement methods. Several performance criteria are utilised to evaluate the proposed method’s efficiency. These include Signal to Noise Ratio (SNR), Mean opinion score (MOS), and visual document image quality assessment (VDIQA) metric called Visual Document Image Quality Assessment Metric (VDQAM). Additional assessment criteria to measure post-processing binarization quality are also discussed with enhanced results based on the Peak signal-to-noise ratio (PSNR), negative rate metric (NRM) and F-measure.Keywords: Image Enhancement, Historical Documents, OCR, Digitisation, Adaptive histogram matchin
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