Text/Non-Text Separation from Handwritten Document Images Using LBP Based Features: An Empirical Study

Ghosh, Sourav; Lahiri, Dibyadwati; Bhowmik, Showmik; Kavallieratou, Ergina; Sarkar, Ram

doi:10.3390/jimaging4040057

Cited by 21 publications

(6 citation statements)

References 25 publications

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“…Next, it is followed by a smoothing process using two-dimensional convolution with a suited binary thinning filter to reduce the number of produced CCs, principally tiny or unintentional instances of unsought CCs caused by some reasons like fast handwriting or overlapping between text and page lines during the handwriting. Next, different relative thresholding methods [2,4] are used to nominate potentials and initially consider them as non-text objects, which are expected to be significantly more prominent than the average possible CC of handwritten texts. Regarding the 'Scribble' (C5) detection and characterization, a similar relative thresholding method is designed to be suited for initial scribble object detection and localization.…”

Section: Analyzing Non-textual Objectsmentioning

confidence: 99%

“…As such, document layout analysis (DLA) is used as a standard preprocessing and an essential prerequisite for developing any document image processing and analysis system. Thus, DLA has emerged as a priority topic and active research domain [3] and has increasingly become a significant interest in numerous research studies [4][5][6][7][8][9]. DLA algorithms can be carried out top-down or bottom-up with respect to their processing order [10].…”

Section: Introductionmentioning

confidence: 99%

“…For this classification, a diversity of preliminary and sophisticated techniques of image processing, computer vision, and machine learning was effectively devoted. Consequently, various research approaches were proposed using different combinations of such functional techniques comprising: anisotropic diffusion with geometric features for historical DLA [17]; local binary pattern (LBP) for text/non-text separation of handwritten documents [4]; contour classification methods and morphological operators for the complex layout of newspapers and magazines [11]; Harris corner detectors for gradientbased manuscript segmentation and reconstruction [19]; homogeneity algorithm and mathematical morphology for page element segmentation [22]; 2D Markovian approach with supplemental textual and spatial information for handwritten letters [6]; and support vector machine (SVM) for text and metadata extraction from Arabic documents [5]. Performing consecutive or cumulative connected component (CC) and pixel analyses on a document image was a typical dominant technique enforced to initially identify regions and then classify them, as adopted by the majority of proposed DLA systems [17,19,22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Semantic Document Layout Analysis of Handwritten Manuscripts

Jaha¹

2023

Computers, Materials &Amp; Continua

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A document layout can be more informative than merely a document's visual and structural appearance. Thus, document layout analysis (DLA) is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives. This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis (SDLA) by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts. The proposed SDLA approach enables the derivation of implicit information and semantic characteristics, which can be effectively utilized in dozens of practical applications for various purposes, in a way bridging the semantic gap and providing more understandable high-level document image analysis and more invariant characterization via absolute and relative labeling. This approach is validated and evaluated on a large dataset of Arabic handwritten manuscripts comprising complex layouts. The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts. It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional, reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.

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Section: Analyzing Non-textual Objectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic Document Layout Analysis of Handwritten Manuscripts

Jaha¹

2023

Computers, Materials &Amp; Continua

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“…Area occupancy is calculated using the equidistant pixels in the distance transform map. In another work, Ghosh et al (2018) 2019) have designed a layout analysis method for complex document images. In their work, they have designed a CNN model that extracts texture-based features for classifying pixels as either text or non-text.…”

Section: Text Non-text Separationmentioning

confidence: 99%

Application of texture-based features for text non-text classification in printed document images with novel feature selection algorithm

et al. 2021

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Text non-text separation is one of the most essential pre-processing steps for any optical character recognition (OCR) system. As an OCR engine can only process texts, the non-texts present in an input document image are required to be suppressed at the initial level. Therefore, to build a complete OCR system, an efficient text non-text separation module needs to be developed. To this end, we have proposed a texture-based feature descriptor followed by a novel feature selection technique for region-based text non-text classification. First, we have incorporated rotation invariant property with local ternary pattern to form a new texture-based feature descriptor, rotation invariant local ternary pattern (RILTP). Next, a novel feature selection technique is proposed which is a modified version of binary particle swarm optimization (BPSO). For the evaluation of the proposed text non-text classification method, we have initially constructed a database consisting of 690 images of text and non-text regions extracted from 70 pages of RDCL 2015 and 75 pages of RDCL 2017 page segmentation competitions databases. In this database, each class contains 345 data samples. The proposed texturebased feature descriptor has obtained an accuracy of 97.09% on this database. Whereas, after applying BPSO, the feature dimension is reduced by approximately 55% and at the same time, the accuracy reaches 97.5%. Furthermore, in this work, another database is also created from Media team document pages to validate the robustness of this method. The second database comprises 100 text and 100 non-text images. The method has achieved 96.28% accuracy when it is trained with the first database and tested with the second database. The comparative study reveals the robustness and strength of the proposed method as it outnumbers many state-of-the-art texture-based features. Besides, the proposed feature selection method is also compared with various standard feature selection methods, and it has been observed that the proposed one outperforms all those methods considered here for comparison.

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“…Once a document image is preprocessed, a next step described in the paper by Ghosh et al [4] consists in separating text components from non-text ones, using a classifier based on LBP features. Following steps may consist in recognizing text components or searching from word queries.…”

mentioning

confidence: 99%