Arabic handwritten text line extraction using connected component analysis from a multi agent perspective

Boulid, Youssef; Souhar, Abdelghani; Elkettani, Mohamed Youssfi

doi:10.1109/isda.2015.7489204

Cited by 9 publications

(7 citation statements)

References 24 publications

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“…F1-Score MS2 = 90% MS2 = 95% Souhar et al [2] Not Reported 93.3% Boulid et al [3] 97.4% 94.3% Boulid et al [4] 95.8% 90.5% Kumar et al [12] 95.6% 90.9% Kumar et al [13] 98.8% Not Reported Zhang et al [14] 98.41% Not Reported The proposed method 97.5%…”

Section: Methodsmentioning

confidence: 99%

“…These areas are then analyzed in order to clustering them into well-aligned text lines. The same can be said for the other [3] approach where a multi-agent system was used to process each step of the line segmentation from de-noising to dealing with touching components. In case of [4], the problem was modeled into a decision making situation.…”

Section: Related Workmentioning

confidence: 99%

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Handwritten Arabic Documents Segmentation into Text Lines using Seam Carving

Daldali¹,

Souhar²

2019

IJIMAI

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Inspired from human perception and common text documents characteristics based on readability constraints, an Arabic text line segmentation approach is proposed using seam carving. Taking the gray scale of the image as input data, this technique offers better results at extracting handwritten text lines without the need for the binary representation of the document image. In addition to its fast processing time, its versatility permits to process a multitude of document types, especially documents presenting low text-to-background contrast such as degraded historical manuscripts or complex writing styles like cursive handwriting. Even if our focus in this paper was on Arabic text segmentation, this method is language independent. Tests on a public database of 123 handwritten Arabic documents showed a line detection rate of 97.5% for a matching score of 90%.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Handwritten Arabic Documents Segmentation into Text Lines using Seam Carving

Daldali¹,

Souhar²

2019

IJIMAI

Self Cite

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“…They have the merit to deal with noise problems and writing variation [7]. The common bottom-up method is based on the connected components [8], [28]. But, as most of top-down methods, bottom-up methods require prior knowledge about the documents, such as text-line inter-spaces, its orientation, etc.…”

Section: Introductionmentioning

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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“…Handwritten documents commonly include multi-skewed text lines, different inter-line distances and touching text lines. There are various methods presented in the literature to tackle the problem of text line segmentation in handwritten document images [1][2][3][4][5][6][7][8][9][10][11]. In general, text line segmentation methods in the literature can be categorized into four main groups: i) projection-based, ii) smearing-based, iii) Hough transform-based, and iv) grouping methods.…”

Section: Introductionmentioning

confidence: 99%

“…By employing a minimum spanning tree clustering on the calculated distances the CCs were grouped to extract text lines. In [7], a CC grouping was introduced using vertical overlapping of CCs to extract Arabic handwritten text lines. Initially, the bounding boxes of CCs were obtained.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Text Line Extraction in Handwritten Document Images

Kiumarsi¹,

Alaei

2018

2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)

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Text line segmentation in handwritten document image, as one of the preliminarily steps for document image recognition, is a challenging problem. In this paper, a hybrid method for text line extraction in handwritten document images is presented. Initially, a connected component (CC) labelling method following by a CC filtering is employed to extract a set of CCs from the input document image. A new distance measure is introduced to compute normal distances between the extracted CCs. By traversing the normal distance matrix from both the right and left directions, halfchains of CCs are constructed. The CCs half-chains are merged to obtain CCs full-chains. From the extracted fullchains separator lines are obtained. A gradient metric is proposed to detect and remove touching text lines. Using remaining separator lines the adaptive projection profile of the image is computed. Based on the projection profile, coarse text line extraction is performed. Finally, a fine text lines extraction is performed by applying a postprocessing step. To evaluate the method, two benchmarks named ICDAR2013 handwriting segmentation contest, and Kannada datasets composed of handwritten document images in English, Greek, Bengali, and Kannada languages were considered for experimentation. Experimental results indicate a promising performance was obtained compared to some of the state-of-the-art methods.

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Arabic handwritten text line extraction using connected component analysis from a multi agent perspective

Cited by 9 publications

References 24 publications

Handwritten Arabic Documents Segmentation into Text Lines using Seam Carving

Handwritten Arabic Documents Segmentation into Text Lines using Seam Carving

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

A Hybrid Method for Text Line Extraction in Handwritten Document Images

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