6 research outputs found
DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning
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
Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΎΠ±ΡΡΠΌΠ½ΠΎΠ³ΠΎ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡ Π°ΡΠΌΠΈΡΡΡΡΠ΅Π³ΠΎ Π²ΠΎΠ»ΠΎΠΊΠ½Π° Π² Π½ΠΈΡΡΡ
ΡΠ»ΠΎΠΈΡΡΡΡ
ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΠ² Ρ ΡΠΊΠ°Π½Π΅Π²ΡΠΌΠΈ Π·Π°ΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠΌΠΈ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎ ΡΡΡΡΠΊΡΡΡΠ΅ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ ΡΠΈΡΡΠΎΠ²ΡΠ΅ ΠΌΠΈΠΊΡΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΡΠ»ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ ΠΏΠΎΠΏΠ΅ΡΠ΅ΡΠ½ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΠ². ΠΠ±ΡΡΠΆΠ΄Π°ΡΡΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΈ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ°ΡΡΡΠΎΠ²ΡΡ
ΠΌΠΈΠΊΡΠΎΡΠΊΠΎΠΏΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ² Π³Π΅ΡΠ΅ΡΠΎΠ³Π΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π°, ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ Ρ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΡΡΠΊΠΎΡΡΡΡ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ ΠΈ ΡΠ°Π·ΠΌΡΡΠΎΡΡΡΡ Π³ΡΠ°Π½ΠΈΡ Β«Π²ΠΎΠ»ΠΎΠΊΠ½ΠΎ-ΡΠ²ΡΠ·ΡΡΡΠ΅Π΅Β». Π ΡΠ΅Π»ΡΡ
ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΡ ΡΡΡΠ΄ΠΎΡΠΌΠΊΠΎΡΡΠΈ ΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΈ ΡΡΡΠΎΠΈΡΡΡ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΠΉ Π°Π²ΡΠΎΡΠ½ΠΊΠΎΠ΄Π΅Ρ. ΠΠ·Π»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΡΠΎΠΏΡΠΎΠ²ΠΎΠΆΠ΄Π°Π΅ΡΡΡ ΡΠΊΠ²ΠΎΠ·Π½ΡΠΌ Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠ°ΡΠΈΠΎΠ½Π½ΡΠΌ ΠΏΡΠΈΠΌΠ΅ΡΠΎΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΡΡΡΠΊΡΡΡΡ ΡΠΈΠΏΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠ³Π»Π΅ΠΏΠ»Π°ΡΡΠΈΠΊΠ°. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ ΡΡΠΊΠΎΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠ²ΡΡΡΠΎΡΠ½ΠΎΠ³ΠΎ Π°Π²ΡΠΎΡΠ½ΠΊΠΎΠ΄Π΅ΡΠ° ΠΈ Ρ
ΠΎΡΠΎΡΠ΅Π΅ ΡΠΎΠ³Π»Π°ΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Ρ ΡΡΠ°ΡΠ΅Π»ΡΠ½ΡΠΌ ΡΡΡΠ½ΡΠΌ Π°Π½Π°Π»ΠΈΠ·ΠΎΠΌ
BiNet:Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks
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
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 β 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 methodβs 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