We address the problem of segmenting and retrieving word images in collections of historical manuscripts given a text query. This is commonly referred to as "word spotting". To this end, we first p ...
@cite_70: 
@cite_29: We address the problem of predicting similarity between a pair of handwritten document images written by different individuals. This has applications related to matching and mining in image collections containing handwritten content. A similarity score is computed by detecting patterns of text re-usages between document images irrespective of the minor variations in word morphology, word ordering, layout and paraphrasing of the content. Our method does not depend on an accurate segmentation of words and lines. We formulate the document matching problem as a structured comparison of the word distributions across two document images. To match two word images, we propose a convolutional neural network (CNN) based feature descriptor. Performance of this representation surpasses the state-of-the-art on handwritten word spotting. Finally, we demonstrate the applicability of our method on a practical problem of matching handwritten assignments.
@cite_32: The segmentation of individual words is a crucial step in several data mining methods for historical handwritten documents. Examples of applications include visual searching for query words (word spotting) and character-by-character text recognition. In this paper, we present a novel method for word segmentation that is adapted from recent advances in computer vision, deep learning and generic object detection. Our method has unique capabilities and it has found practical use in our current research project. It can easily be trained for different kinds of historical documents, uses full gray scale information, does not require binarization as pre-processing or prior segmentation of individual text lines. We evaluate its performance using established error metrics, previously used in competitions for word segmentation, and demonstrate its usefulness for a 15th century handwritten document.
@cite_84: 
@cite_27: In this article we introduce a text search algorithm designed for ancient manuscripts. Word-spotting is the best alternative to word recognition on this type of document. Our method is based on differential features that are compared using a cohesive elastic matching method, based on zones of interest in order to match only the informative parts of the words. Thus we improved both the accuracy and the runtime of the word-spotting process. The proposed method is tested on medieval manuscripts of Latin and Semitic alphabets as well as on more recent manuscripts.
@cite_81: 
@cite_86: The generation of word hypotheses for segmentation-free word spotting on document level is usually subject to heuristic expert design. This involves strong assumptions about the visual appearance of text in the document images. In this paper we propose to generate hypotheses with text detectors. In order to do so, we present three detectors that are based on SIFT contrast scores, CNN region classification scores and attribute activation maps. The uncertainty in the detector scores is modeled with the extremal regions method. Retrieving word hypotheses is based on PHOC representations which we compute with the TPP-PHOCNet. We evaluate our method on the George Washington dataset and the ICFHR 2016 KWS competition benchmarks. In the evaluation we show that high word detection rates can be achieved. This is a prerequisite for high retrieval performance that is competitive with the state-of-the-art.
@cite_34: 
@cite_10: A simple and efficient pipeline for word spotting in handwritten documents is proposed. The method allows for extremely rapid querying, while still maintaining high accuracy. The dataset images that are to be queried are preprocessed by a simple binarization operation, followed by the extraction of multiple overlapping candidate targets. Each binary target, as well as the binarized query, is resized to fit a fixed-size rectangle and represented by conventional image descriptors. Then, a cosine similarity operator—followed by maximum pooling over random groups—is used to represent each target or query as a concise 250D vector. Retrieval is performed in a fraction of a second by nearest-neighbor search within that space, followed by a simple suppression of extra overlapping candidates.
The second category are methods using connected components @cite_27 @cite_81 @cite_84 @cite_70 @cite_34 . In @cite_27 , connected components in the shape of vertical strokes are extracted by performing mathematical morphology to separate characters. A popular approach is to binarize the image, extract connected components, and group them in a bottom-up fashion using heuristics and finally extract bounding boxes @cite_10 @cite_81 . A similar approach is used in @cite_29 for matching entire documents using distributions of word images. A combination of the two approaches is used in @cite_86 . Here, extremal regions are extracted on top of text presence scores computed using a sliding window. A related approach is applied to word segmentation in @cite_32 , where morphological closing is used using a variety of different kernel sizes to connect characters into words and extract bounding boxes. These methods still over-segment the manuscript, but the number of proposals is typically fewer than for sliding window based methods. They are also more robust to small input shifts as the connected components provide a kind of attention. The downsides include a sensitivity to physical degradations like ink blotches and difficulty with densely written manuscripts.