Using Scale-Space Anisotropic Smoothing for Text Line Extraction in Historical Documents

Cohen, Rafi; Dinstein, I.; El-Sana, Jihad; Kedem, Klara

doi:10.1007/978-3-319-11758-4_38

Cited by 19 publications

(15 citation statements)

References 17 publications

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“…For text-line segmentation, Cohen & al. [11] used a bottom-up method. Their system starts by applying a multi-scale anisotropic second derivative of Gaussian filter bank to enhance text regions.…”

Section: Related Workmentioning

confidence: 99%

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

Neche

Belaïd

Kacem-Echi

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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One of the most important steps in a handwriting recognition system is text-line and word segmentation. But, this step is made difficult by the differences in handwriting styles, problems of skewness, overlapping and touching of text and the fluctuations of text-lines. It is even more difficult for ancient and calligraphic writings, as in Arabic manuscripts, due to the cursive connection in Arabic text, the erroneous position of diacritic marks, the presence of ascending and descending letters, etc. In this work, we propose an effective segmentation of Arabic handwritten text into text-lines and words, using deep learning. For text-line segmentation, we used an RU-net which allows a pixel-wise classification to separate text-lines pixels from the background ones. For word segmentation, we resorted to the text-line transcription, as we have not got a ground truth at word level. A BLSTM-CTC (Bidirectional Long Short Term Memory followed by a Connectionist Temporal Classification) is then used to perform the mapping between the transcription and text-line image, avoiding the need of the input segmentation. A CNN (Convolutional Neural Network) precedes the BLST-CTC to extract the features and to feed the BLSTM with the essential of the text-line image. Tested on the standard KHATT Arabic database, the experimental results confirm a segmentation success rate of no less than 96.7% for text-lines and 80.1% for words.

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Section: Related Workmentioning

confidence: 99%

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

Neche

Belaïd

Kacem-Echi

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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“…Recently, there have appeared new techniques addressing the text line segmentation problem on historical documents: [4], [5], [6], [7], [8], [9]. Likforman-Sulem et al [10] presented a complete study with several techniques for this problem and their taxonomy.…”

Section: Related Workmentioning

confidence: 99%

Complete System for Text Line Extraction Using Convolutional Neural Networks and Watershed Transform

Pastor-Pellicer

Afzal

Liwicki

et al. 2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

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We present a novel Convolutional Neural Networkbased 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.

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“…Recently, we suggested a text line extraction technique based on scale-space filtering for horizontal text lines [28]. In this section we briefly review the changes made to our algorithm for handling curled text lines.…”

Section: Line Segmentationmentioning

confidence: 99%

“…The removal becomes feasible based on an extension of the energy minimization framework introduced in [29]. The final step of merging broken line segments is yielded by employing a Minimum Spanning Tree algorithm [28] over the segment edges as appears in Figure 4(f).…”

Section: Line Segmentationmentioning

confidence: 99%

Simplifying the reading of historical manuscripts

Asi

Cohen

Kedem

et al. 2015

2015 13th International Conference on Document Analysis and Recognition (ICDAR)

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Complex document layouts pose prominent challenges for document image understanding algorithms. These layouts impose irregularities on the location of text paragraphs which consequently induces difficulties in reading the text. In this paper we present a robust framework for analyzing historical manuscripts with complex layouts. This framework aims to provide a convenient reading experience for historians through topnotch algorithms for text localization, classification and dewarping. We segment text into spatially coherent regions and text-lines using texture-based filters and refine this segmentation by exploiting Markov Random Fields (MRFs). A principled technique is presented for dewarping curvy text regions using a non-linear geometric transformation. The framework has been validated using a subset of a publicly available dataset of historical documents and it provided promising results.

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Using Scale-Space Anisotropic Smoothing for Text Line Extraction in Historical Documents

Cited by 19 publications

References 17 publications

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

Complete System for Text Line Extraction Using Convolutional Neural Networks and Watershed Transform

Simplifying the reading of historical manuscripts

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