Automatic Document Image Binarization using Bayesian Optimization

Document image binarization is often a challenging task due to various forms of degradation. Although there exist several binarization techniques in literature, the binarized image is typically sensitive to control parameter settings of the employed technique. This paper presents an automatic document image binarization algorithm to segment the text from heavily degraded document images. The proposed technique uses a two band-pass filtering approach for background noise removal, and Bayesian optimization for automatic hyperparameter selection for optimal results. The effectiveness of the proposed binarization technique is empirically demonstrated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets

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

Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks

The efficient segmentation of foreground text information from the background in degraded color document images is a hot research topic. Due to the imperfect preservation of ancient documents over a long period of time, various types of degradation, including staining, yellowing, and ink seepage, have seriously affected the results of image binarization. In this paper, a three-stage method is proposed for image enhancement and binarization of degraded color document images by using discrete wavelet transform (DWT) and generative adversarial network (GAN). In Stage-1, we use DWT and retain the LL subband images to achieve the image enhancement. In Stage-2, the original input image is split into four (Red, Green, Blue and Gray) single-channel images, each of which trains the independent adversarial networks. The trained adversarial network models are used to extract the color foreground information from the images. In Stage-3, in order to combine global and local features, the output image from Stage-2 and the original input image are used to train the independent adversarial networks for document binarization. The experimental results demonstrate that our proposed method outperforms many classical and state-of-the-art (SOTA) methods on the Document Image Binarization Contest (DIBCO) dataset. We release our implementation code at https://github.com/abcpp12383/ThreeStageBinarization

Binarisation Algorithms Analysis on Document and Natural Scene Images

The binarisation plays an important role in a system for text extraction from images which is a prominent area in digital image processing. The primary goal of the binarisation techniques are to covert colored and gray scale image into black and white image so that overall computational overhead can be minimized. It has great impact on performance of the system for text extraction from image. Such system has number of applications like navigation system for visually impaired persons, automatic text extraction from document images, and number plate detection to enforcement traffic rules etc. The present study analysed the performance of well known binarisation algorithms on degraded documents and camera captured images. The statistical parameters namely Precession, Recall and F-measure and PSNR are used to evaluate the performance. To find the suitability of the binarisation method for text preservation in natural scene images, we have also considered visual observation DOI: 10.17762/ijritcc2321-8169.15083

DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF

For capturing colored document images, e.g. posters and magazines, it is common that multiple degradations such as shadows, wrinkles, etc., are simultaneously introduced due to external factors. Restoring multi-degraded colored document images is a great challenge, yet overlooked, as most existing algorithms focus on enhancing color-ignored document images via binarization. Thus, we propose DocStormer, a novel algorithm designed to restore multi-degraded colored documents to their potential pristine PDF. The contributions are: firstly, we propose a "Perceive-then-Restore" paradigm with a reinforced transformer block, which more effectively encodes and utilizes the distribution of degradations. Secondly, we are the first to utilize GAN and pristine PDF magazine images to narrow the distribution gap between the enhanced results and PDF images, in pursuit of less degradation and better visual quality. Thirdly, we propose a non-parametric strategy, PFILI, which enables a smaller training scale and larger testing resolutions with acceptable detail trade-off, while saving memory and inference time. Fourthly, we are the first to propose a novel Multi-Degraded Colored Document image Enhancing dataset, named MD-CDE, for both training and evaluation. Experimental results show that the DocStormer exhibits superior performance, capable of revitalizing multi-degraded colored documents into their potential pristine digital versions, which fills the current academic gap from the perspective of method, data, and task

Handwritten and printed text separation in historical documents

Historical documents present many challenges for Optical Character Recognition Systems (OCR), especially documents of poor quality containing handwritten annotations, stamps, signatures, and historical fonts. As most OCRs recognize either machine-printed or handwritten texts, printed and handwritten parts have to be separated before using the respective recognition system. This thesis addresses the problem of segmentation of handwritings and printings in historical Latin text documents. To alleviate the problem of lack of data containing handwritten and machine-printed components located on the same page or even overlapping each other as well as their pixel-wise annotations, the data synthesis method proposed in [12] was applied and new datasets were generated. The newly created images and their pixel-level labels were used to train Fully Convolutional Model (FCN) introduced in [5]. The newly trained model has shown better results in the separation of machine-printed and handwritten text in historical documents

Image Enhancement Background for High Damage Malay Manuscripts using Adaptive Threshold Binarization

Jawi Manuscripts handwritten which are kept at Malaysia National Library (MNL), has aged over decades. Regardless of the intensive sustainable process conducted by MNL, these manuscripts are still not maintained in good quality, and neither can easily be read nor better view. Even thought, many states of the art methods have developed for image enhancement, none of them can solve extremely bad quality manuscripts. The quality of old Malay Manuscripts can be categorize into three types, namely: the background image is uneven, image effects and image effects expand patch. The aim of this paper is to discuss the methods used to value add the quality of the manuscript.  Our propose methods consist of several main methods, such as: Local Adaptive Equalization, Image Intensity Values, Automatic Threshold PP, and Adaptive Threshold Filtering. This paper is intend to achieve a better view image that geared to ease reading. Error Bit Phase achievement (TKB) has a smaller error value for proposed method (Adaptive Threshold Filtering Process / PAM) namely 0.0316 compared with Otsu’s Threshold Method / MNAO, Binary Threshold Value Method / MNAP, and Automatic Local Threshold Value Method / MNATA. The precision achievement (namely on ink bleed images) is using a proposed method more than 95% is compared with the state of the art methods MNAO, MNAP, MNATA and their performances are 75.82%, 90.68%, and 91.2% subsequently.  However, this paper’s achievement is using a proposed method / PAM, MNAO, MNAP, and MNATA for correspondingly the image of ink bleed case are 45.74%, 54.80%, 53.23% and 46.02%.  Conclusion, the proposed method produces a better character shape in comparison to other methods

Automated framework for robust content-based verification of print-scan degraded text documents

Fraudulent documents frequently cause severe financial damages and impose security breaches to civil and government organizations. The rapid advances in technology and the widespread availability of personal computers has not reduced the use of printed documents. While digital documents can be verified by many robust and secure methods such as digital signatures and digital watermarks, verification of printed documents still relies on manual inspection of embedded physical security mechanisms.The objective of this thesis is to propose an efficient automated framework for robust content-based verification of printed documents. The principal issue is to achieve robustness with respect to the degradations and increased levels of noise that occur from multiple cycles of printing and scanning. It is shown that classic OCR systems fail under such conditions, moreover OCR systems typically rely heavily on the use of high level linguistic structures to improve recognition rates. However inferring knowledge about the contents of the document image from a-priori statistics is contrary to the nature of document verification. Instead a system is proposed that utilizes specific knowledge of the document to perform highly accurate content verification based on a Print-Scan degradation model and character shape recognition. Such specific knowledge of the document is a reasonable choice for the verification domain since the document contents are already known in order to verify them.The system analyses digital multi font PDF documents to generate a descriptive summary of the document, referred to as \Document Description Map" (DDM). The DDM is later used for verifying the content of printed and scanned copies of the original documents. The system utilizes 2-D Discrete Cosine Transform based features and an adaptive hierarchical classifier trained with synthetic data generated by a Print-Scan degradation model. The system is tested with varying degrees of Print-Scan Channel corruption on a variety of documents with corruption produced by repetitive printing and scanning of the test documents. Results show the approach achieves excellent accuracy and robustness despite the high level of noise