In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and...

To handle the variety of scene characters, we propose a cooperative multiple-hypothesis framework which consists of an image operator set module, an Optical Character Recognition (OCR) module and an integration module. Multiple image operators activated by multiple parameters probe suspected character regions. The OCR module is then applied to each suspected region and returns multiple candidat...

The objective of this study is to propose a new method for text region localization and character extraction in natural scene images with complex background. In this paper, a hybrid methodology is suggested which extracts multilingual text from natural scene image with cluttered backgrounds. The proposed approach involves four steps. First, potential text regions in an image are extracted based...

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Text line extraction is an essential preprocessing step in many handwritten document image analysis tasks. It includes detecting text lines a and segmenting the regions of each detected line. Deep learning-based methods are frequently used for detection. However, only limited number tackle problems detection segmentation together. This paper proposes holistic method that applies Mask R-CNN extr...

This paper describes on-going work in the development of a document layout analysis system based on text line segments. It describes two novel algorithms: gapped text line finding, which can identify text line segments, taking into account per-text line font information for the determination of where text line segments break, and reading order recovery for text line segments using topological s...

Conditional random fields were trained to detect marker words for negation and speculation in two corpora belonging to two very different domains: clinical text and consumer review text. For the corpus of clinical text, marker words for speculation and negation were detected with results in line with previously reported interannotator agreement scores. This was also the case for speculation mar...

We propose a novel approach for finding text in images by using ridges at several scales. A text string is modelled by a ridge at a coarse scale representing its center line and numerous short ridges at a smaller scale representing the skeletons of characters. Skeleton ridges have to satisfy geometrical and spatial constraints such as the perpendicularity or non-parallelism to the central ridge...

This paper compares the efficacy of two anomaly detection classifiers with respect to the classification of processes as either intrusive or non-intrusive. To the task of process classification, both classifiers treat processes as system call sequences, encode those system call sequences as text documents, and apply the k-nearest neighbor text categorization method to classify the processes. In...