Document Layout Analysis is a fundamental step in Handwritten Text Processing systems, from the extraction of the text lines to the type of zone it belongs to. We present a system based on artificial neural networks which is able to determine not only the baselines of text lines present in the document, but also performs geometric and logic layout analysis of the document. Experiments in three different datasets demonstrate the potential of the method and show competitive results with respect to state-of-the-art methods.
Methods such as those presented in @cite_17 @cite_45 @cite_18 @cite_20 @cite_32 rely on connected components extraction after some filtering stage, while other methods such as @cite_4 @cite_28 use a tracer function to separate the different text lines after applying a blur to the input image. Other methods rely on Hidden Markov Models @cite_34 @cite_41 @cite_15 or Recurrent Neural Networks @cite_25 to model the vertical sequential structure of the text lines, or Convolutional Neural Networks @cite_44 @cite_0 @cite_21 to classify each pixel of the image between text line and non-text line.
@cite_18 @cite_4 @cite_28 @cite_41 @cite_21 @cite_32 @cite_44 @cite_0 @cite_45 @cite_15 @cite_34 @cite_25 @cite_20 @cite_17
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Text line extraction is vital pre-requisite for various document processing tasks. This paper presents a novel approach for text line extraction which is based on Gaussian scale space and dedicated binarization that utilize the inherent structure of smoothed text document images. It enhances the text lines in the image using multi-scale anisotropic second derivative of Gaussian filter bank at the average height of the text line. It then applies a binarization, which is based on component-tree and is tailored towards line extraction. The final stage of the algorithm is based on an energy minimization framework for removing spurious text line and assigning connected components to lines. We have tested our approach on various datasets written in different languages at range of image quality and received high detection rates, which outperform state-of-the-art algorithms. Our MATLAB code is publicly available. (http: www.cs.bgu.ac.il rafico LineExtraction.zip) In this paper we present an approach for text line analysis and detection in handwritten documents based on Hidden Markov Models, a technique widely used in other handwritten and speech recognition tasks. It is shown that text line analysis and detection can be solved using a more formal methodology in contraposition to most of the proposed heuristic approaches found in the literature. Our approach not only provides the best position coordinates for each of the vertical page regions but also labels them, in this manner surpassing the traditional heuristic methods. In our experiments we demonstrate the performance of the approach (both in line analysis and detection) and study the impact of increasingly constrained "vertical layout language models" on text line detection accuracy. Through this experimentation we also show the improvement in quality of the baselines yielded by our approach in comparison with a state-of-the-art heuristic method based on vertical projection profiles. In recent years there have been multiple successful attempts tackling document processing problems separately by designing task specific hand-tuned strategies. We argue that the diversity of historical document processing tasks prohibits to solve them one at a time and shows a need for designing generic approaches in order to handle the variability of historical series. In this paper, we address multiple tasks simultaneously such as page extraction, baseline extraction, layout analysis or multiple typologies of illustrations and photograph extraction. We propose an open-source implementation of a CNN-based pixel-wise predictor coupled with task dependent post-processing blocks. We show that a single CNN-architecture can be used across tasks with competitive results. Moreover most of the task-specific post-precessing steps can be decomposed in a small number of simple and standard reusable operations, adding to the flexibility of our approach. We present a novel Convolutional Neural Network based method for the extraction of text lines, which consists of an initial Layout Analysis followed by the estimation of the Main Body Area (i.e., the text area between the baseline and the corpus line) for each text line. Finally, a region-based method using watershed transform is performed on the map of the Main Body Area for extracting the resulting lines. We have evaluated the new system on the IAM-HisDB, a publicly available dataset containing historical documents, outperforming existing learning-based text line extraction methods, which consider the problem as pixel labelling problem into text and non-text regions. Document layout analysis is an important task needed for handwritten text recognition among other applications. Text layout commonly found in handwritten legacy documents is in the form of one or more paragraphs composed of parallel text lines. An approach for handwritten text line detection is presented which uses machine-learning techniques and methods widely used in natural language processing. It is shown that text line detection can be accurately solved using a formal methodology, as opposed to most of the proposed heuristic approaches found in the literature. Experimental results show the impact of using increasingly constrained "vertical layout language models" in text line detection accuracy. The detection of text lines, as a first processing step, is critical in all text recognition systems. State-of-the-art methods to locate lines of text are based on handcrafted heuristics fine-tuned by the image processing community's experience. They succeed under certain constraints; for instance the background has to be roughly uniform. We propose to use more “agnostic” Machine Learning-based approaches to address text line location. The main motivation is to be able to process either damaged documents, or flows of documents with a high variety of layouts and other characteristics. A new method is presented in this work, inspired by the latest generation of optical models used for text recognition, namely Recurrent Neural Networks. As these models are sequential, a column of text lines in our application plays here the same role as a line of characters in more traditional text recognition settings. A key advantage of the proposed method over other data-driven approaches is that compiling a training dataset does not require labeling line boundaries: only the number of lines are required for each paragraph. Experimental results show that our approach gives similar or better results than traditional handcrafted approaches, with little engineering efforts and less hyper-parameter tuning. In this paper, we present a new text line extraction method for handwritten Arabic documents. The proposed technique is based on a generalized adaptive local connectivity map (ALCM) using a steerable directional filter. The algorithm is designed to solve the particularly complex problems seen in handwritten documents such as fluctuating, touching or crossing text lines. The proposed algorithm consists of three steps. Firstly, a steerable filter is used to probe and determine foreground intensity along multiple directions at each pixel while generating the ALCM. The ALCM is then binarized using an adaptive thresholding algorithm to get a rough estimate of the location of the text lines. In the second step, connected component analysis is used to classify text and non text patterns in the generated ALCM to refine the location of the text lines. Finally, the text lines are separated by superimposing the text line patterns in the ALCM on the original document image and extracting the connected components covered by the pattern mask. Analysis of experimental results on the DARPA MADCAT Arabic handwritten document data indicate that the method is robust and is capable of correctly isolating handwritten text lines even on challenging document images.