Segmentation of Heterogeneous Documents into Homogeneous Components using Morphological Operations

Barna, Nasid Habib; Erana, Tisa Islam; Ahmed, Shabbir; Heickal, Hasnain

doi:10.1109/icis.2018.8466395

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…In this case, we used the noise correction procedure described in [6] and illustrated in Figure 6. We use morphological operators [36][37][38] for reducing and eliminating noise regions in the images. Our procedure uses Dilation and Erosion operations to remove pattern lines and other document-shaping structures as well as to eliminate small noisy regions.…”

Section: Noise Correctionmentioning

confidence: 99%

Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

et al. 2021

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“Wasan” is the collective name given to a set of mathematical texts written in Japan in the Edo period (1603–1867). These documents represent a unique type of mathematics and amalgamate the mathematical knowledge of a time and place where major advances where reached. Due to these facts, Wasan documents are considered to be of great historical and cultural significance. This paper presents a fully automatic algorithmic process to first detect the kanji characters in Wasan documents and subsequently classify them using deep learning networks. We pay special attention to the results concerning one particular kanji character, the "ima" kanji, as it is of special importance for the interpretation of Wasan documents. As our database is made up of manual scans of real historical documents, it presents scanning artifacts in the form of image noise and page misalignment. First, we use two preprocessing steps to ameliorate these artifacts. Then we use three different blob detector algorithms to determine what parts of each image belong to kanji Characters. Finally, we use five deep learning networks to classify the detected kanji. All the steps of the pipeline are thoroughly evaluated, and several options are compared for the kanji detection and classification steps. As ancient kanji database are rare and often include relatively few images, we explore the possibility of using modern kanji databases for kanji classification.Experiments are run on a dataset containing 100 Wasan book pages. We compare the performance of three blob detector algorithms for kanji detection obtaining 79.60% success rate with 7.88% false positive detections. Furthermore, we study the performance of five well-known deep learning networks and obtain 99.75% classification accuracy for modern kanji and 90.4% for classical kanji. Finally, our full pipeline obtains 95% correct detection and classification of the "ima" kanji with 3% False positives.

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Section: Noise Correctionmentioning

confidence: 99%

Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

et al. 2021

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“…Due to the importance of pdf documents in day-to-day services, much research has been done. One of these recent research conducted by NH Barna et al [27], that analyzed various components of documents using opening and dilation and other morphological operations. The method was segmented printed documents into text, image, table, and cell using the bounding box technique.…”

Section: Morphological Operatorsmentioning

confidence: 99%

Off-Line Arabic Handwritten Words Segmentation using Morphological Operators

AbdAllah¹,

Viriri²

2020

SIPIJ

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The main aim of this study is the assessment and discussion of a model for hand-written Arabic through segmentation. The framework is proposed based on three steps: pre-processing, segmentation, and evaluation. In the pre-processing step, morphological operators are applied for Connecting Gaps (CGs) in written words. Gaps happen when pen lifting-off during writing, scanning documents, or while converting images to binary type. In the segmentation step, first removed the small diacritics then bounded a connected component to segment offline words. Huge data was utilized in the proposed model for applying a variety of handwriting styles so that to be more compatible with real-life applications. Consequently, on the automatic evaluation stage, selected randomly 1,131 images from the IESK-ArDB database, and then segmented into sub-words. After small gaps been connected, the model performance evaluation had been reached 88% against the standard ground truth of the database. The proposed model achieved the highest accuracy when compared with the related works.

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A Novel Approach for Newspaper Block Segmentation using Run-Length Smoothing Algorithm

Soma¹,

Shilpa²

2022

2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC)

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Segmentation of Heterogeneous Documents into Homogeneous Components using Morphological Operations

Cited by 3 publications

References 4 publications

Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

Automatic Processing of Historical Japanese Mathematics (Wasan) Documents

Off-Line Arabic Handwritten Words Segmentation using Morphological Operators

A Novel Approach for Newspaper Block Segmentation using Run-Length Smoothing Algorithm

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