Separability versus prototypicality in handwritten word-image retrieval

Oosten, Jean-Paul van; Schomaker, Lambert

doi:10.1016/j.patcog.2013.09.006

Cited by 26 publications

(18 citation statements)

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“…The second dataset belongs to the National Archive of the Netherlands, named KdK (Het Kabinet der Koningin or Dutch Queen's Office) [15,20]. The manuscript was written between years 1798 and 1988, and the year 1903 was used.…”

Section: Datasetsmentioning

confidence: 99%

“…Ideally, the approach should be robust, require a minimum of human intervention with a limited set of hyper-parameter settings (architectures), and minimum linguistic resources. For evaluation, we use a standard benchmark public dataset, RIMES [19], a historical handwritten dataset, KdK [15,20], and the standard public benchmark George Washington dataset (GW [21]). The three datasets differ in historical period and language.…”

Section: Introductionmentioning

confidence: 99%

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A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

Ameryan

Schomaker

2021

Neural Comput & Applic

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The strength of long short-term memory neural networks (LSTMs) that have been applied is more located in handling sequences of variable length than in handling geometric variability of the image patterns. In this paper, an end-to-end convolutional LSTM neural network is used to handle both geometric variation and sequence variability. The best results for LSTMs are often based on large-scale training of an ensemble of network instances. We show that high performances can be reached on a common benchmark set by using proper data augmentation for just five such networks using a proper coding scheme and a proper voting scheme. The networks have similar architectures (convolutional neural network (CNN): five layers, bidirectional LSTM (BiLSTM): three layers followed by a connectionist temporal classification (CTC) processing step). The approach assumes differently scaled input images and different feature map sizes. Three datasets are used: the standard benchmark RIMES dataset (French); a historical handwritten dataset KdK (Dutch); the standard benchmark George Washington (GW) dataset (English). Final performance obtained for the word-recognition test of RIMES was 96.6%, a clear improvement over other state-of-the-art approaches which did not use a pre-trained network. On the KdK and GW datasets, our approach also shows good results. The proposed approach is deployed in the Monk search engine for historical-handwriting collections.

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Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

Ameryan

Schomaker

2021

Neural Comput & Applic

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“…12. Detected junctions in a historical document from Monk [49,50]. Note that all the junctions are normalized into a fixed size in order to improve visualization.…”

Section: Performance Of Juncletsmentioning

confidence: 99%

“…Junction retrieval, which is similar to wordimage retrieval [50], is defined as: given a query junction, the top of the sorted list is obtained based on a large collection of junction instances. Because there is no such data set about junction retrieval, in this section, we give visual results.…”

Section: Junction Retrievalmentioning

confidence: 99%

Junction detection in handwritten documents and its application to writer identification

Wiering

Schomaker

2015

Pattern Recognition

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102

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“…The collected database and modeling are not only relevant to the problem of dating, but also help to develop character and word models for classification and retrieval [4], [6].…”

Section: Modeling Handwritten Style Processesmentioning

confidence: 99%

Towards Style-Based Dating of Historical Documents

Samara

Burgers

et al. 2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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Abstract-Estimating the date of undated medieval manuscripts by evaluating the script they contain, using document image analysis, is helpful for scholars of various disciplines studying the Middle Ages. However, there are, as yet, no systems to automatically and effectively infer the age of historical scripts using machine learning methods. To build a system to date medieval documents is a challenging problem in several aspects: 1) As yet, no suitable reference dataset of medieval handwriting exists; 2) relatively little is known about the evolution of writing styles in the Middle Ages, and especially in the later Middle Ages. Our Medieval Paleographic Scale (MPS) project aims at solving these problems. We have collected a corpus of charters from the Medieval Dutch language area, dating from the period 1300 to 1550. A global and local regression method is proposed for learning and estimating the year in which these documents were written, using several features which have been successfully used in writer identification. The proposed system can serve as a basic tool for the medievalist or paleographer. The experimental results of the proposed method demonstrate its effectiveness.

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Separability versus prototypicality in handwritten word-image retrieval

Cited by 26 publications

References 18 publications

A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

Junction detection in handwritten documents and its application to writer identification

Towards Style-Based Dating of Historical Documents

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