The PASCAL Visual Object Classes Challenge

Pattern recognition

https://users.iit.demokritos.gr/~bgat/cr1115.pdf

Text line and word segmentation of handwritten documents

A pure adversarial optical character recognition (OCR) approach in identifying text content in images. An image and a search term are input to a pure adversarial OCR module, which searches the image for presence of the search term. The image may be extracted from an email by an email processing engine. The OCR module may split the image into several character-blocks that each has a reasonable probability of containing a character (e.g., an ASCII character). The OCR module may form a sequence of blocks that represent a candidate match to the search term and calculate the similarity of the candidate sequence to the search term. The OCR module may be configured to output whether or not the search term is found in the image and, if applicable, the location of the search term in the image.

Pure adversarial approach for identifying text content in images

ICDAR 2009 Handwriting Segmentation Contest was organized in the context of ICDAR2009 conference in order to record recent advances in off-line handwriting segmentation. The contest includes handwritten document images produced by many writers in several languages (English, French, German and Greek). These images are manually annotated in order to produce the ground truth which corresponds to the correct text line and word segmentation result. For the evaluation, a well-established approach is used based on counting the number of matches between the entities detected by the segmentation algorithm and the entities in the ground truth. This paper describes the contest details including the dataset, the ground truth and the evaluation criteria and presents the results of the 12 participating methods as well as of two state-of-the-art algorithms. A description of the winning algorithms is also given.

ICDAR2009 handwriting segmentation contest

In this paper, we propose a novel technique for word spotting in historical printed documents combining synthetic data and user feedback. Our aim is to search for keywords typed by the user in a large collection of digitized printed historical documents. The proposed method consists of the following stages: (1) creation of synthetic image words; (2) word segmentation using dynamic parameters; (3) efficient feature extraction for each word image and (4) a retrieval procedure that is optimized by user feedback. Experimental results prove the efficiency of the proposed approach.

/pdf/keyword-guided-word-spotting-in-historical-printed-documents-2m0nk7e8v0.pdf

Keyword-guided word spotting in historical printed documents using synthetic data and user feedback

In this paper, we propose a novel segmentation-free approach for keyword search in historical typewritten documents combining image preprocessing, synthetic data creation, word spotting and user feedback technologies. Our aim is to search for keywords typed by the user in a large collection of digitized typewritten historical documents. The proposed method is based on: (i) image preprocessing for image binarization and enhancement, noisy border and frame removal, orientation and skew correction; (ii) creation of synthetic image words from keywords typed by the user; (Hi) word segmentation using dynamic parameters; (iv) efficient feature extraction for each image word and (v) a retrieval procedure that is optimized by user's feedback. Experimental results prove the efficiency of the proposed approach.

/pdf/a-segmentation-free-approach-for-keyword-search-in-4n723ws6lf.pdf

A segmentation-free approach for keyword search in historical typewritten documents

Recognition of Old Greek Early Christian manuscripts is essential for efficient content exploitation of the valuable Old Greek Early Christian historical collections. In this paper, we focus on the problem of recognizing Old Greek manuscripts and propose a novel recognition technique that has been tested in a large number of important historical manuscript collections which are written in lowercase letters and originate from St. Catherine’s Mount Sinai Monastery. Based on an open and closed cavity character representation, we propose a novel, segmentation-free, fast and efficient technique for the detection and recognition of characters and character ligatures. First, we detect open and closed cavities that exist in the skeletonized character body. Then, the classification of a specific character or character ligature is based on the protrusible segments that appear in the topological description of the character skeletons. Experimental results prove the efficiency of the proposed approach.

/pdf/an-old-greek-handwritten-ocr-system-based-on-an-efficient-4jugi0nez4.pdf

An old greek handwritten OCR system based on an efficient segmentation-free approach

In this paper, a two-step segmentation-free word spotting method for historical printed documents is presented. The first step involves a minimum distance matching between a query keyword image and a document page image using keypoint correspondences. In the second step of the method, the matched keypoints on the document image serve as indicators for creating candidate image areas. The query keyword image is matched against the candidate image areas in order to properly estimate the bounding boxes of the detected word instances. The method is evaluated using two datasets of different languages and is compared against segmentation-free state-of-the-art methods. The experimental results show that the proposed method outperforms significantly the competitive approaches.

A segmentation-free word spotting method for historical printed documents

Writer identification and verification can be viewed as a classification problem, where each writer represents a class We propose a classifier for offline, text-independent, and segmentation-free writer identification based on the Local Naive Bayes Nearest-Neighbour (Local NBNN) classification Our proposed method takes into consideration the particularity of handwriting patterns by adding a constraint to prevent the matching of irrelevant keypoints Furthermore, a normalisation factor is proposed to cope with the prevalent problem of unbalanced data The method has been evaluated on several public datasets of different writing systems and state-of-the-art results are shown to be improved

Thomas Konidaris

Papers

Keyword-guided word spotting in historical printed documents using synthetic data and user feedback

A segmentation-free approach for keyword search in historical typewritten documents

An old greek handwritten OCR system based on an efficient segmentation-free approach

A segmentation-free word spotting method for historical printed documents

Normalised Local Naïve Bayes Nearest-Neighbour Classifier for Offline Writer Identification