Text Extraction in Document Images: Highlight on Using Corner Points

Yadav, Vikas; Ragot, Nicolas

doi:10.1109/das.2016.67

Cited by 19 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4. Though the technique is simple compared to other proposed [37,38], it gives an excellent result with 100% accuracy. Fig.…”

Section: Checking Systemmentioning

confidence: 97%

Image Purification Technique for Myanmar OCR Applying Skew Angle Detection and Free Skew

Lwin

2019

IJSRST

View full text Add to dashboard Cite

Optical Character Recognition (OCR) is a technology widely adopted for automatic translation of hardcopy text to editable text. The language dependence of the technology makes it far less developed for less popular languages like Myanmar language. Also, the uniqueness and complexity of the Myanmar text system such as touching and complex characters have continued to pose serious challenges to several OCR investigators. In this paper, we propose a new technique to development Myanmar OCR system. Our technique implement skew angle detection and free skew, noisy border correction, extra page elimination, line segmentation from scanned images of Myanmar text. Performance of the proposed method is tested with 430 documents comprising different printed and handwritten Myanmar text of various fonts, sizes, multi-column, tables, stamps or photos, background effects. Our method give an accuracy of 100% for line segmentation and 99.92% for skew angle detection and free skew. The ability of our method to effectively implement global and local skew angle detection, free skew and line segmentation in different handwritten and digital text images of the Myanmar character set with high accuracies confirms the robustness of the technique, its reliability and its suitability for application in many other related languages.

show abstract

“…4. Though the technique is simple compared to other proposed [37,38], it gives an excellent result with 100% accuracy. Fig.…”

Section: Checking Systemmentioning

confidence: 97%

Image Purification Technique for Myanmar OCR Applying Skew Angle Detection and Free Skew

Lwin

2019

IJSRST

View full text Add to dashboard Cite

show abstract

“…MAP, for a set of queries, is the mean of the average precision scores for each query. To define the score, we first define Precision ( ) and Recall ( ), as in Equations ( 18) and (19), respectively.…”

Section: Performance Measurementioning

confidence: 99%

“…Here, it should be noted that the techniques that extract words from document images might be erroneous sometimes and can add some computational overhead if a segmentation-based approach is adopted. However, the state-of-the-art word extraction methods [ 18 , 19 , 20 ] that perform the tasks with good efficiency on complex documents, while consuming less time, could be used to get rid of these issues. For a similar reason, text line-based techniques are computationally more expensive when compared to word-based methods.…”

Section: Introductionmentioning

confidence: 99%

Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Kundu

Malakar²,

Geem

et al. 2021

Sensors

View full text Add to dashboard Cite

Handwritten keyword spotting (KWS) is of great interest to the document image research community. In this work, we propose a learning-free keyword spotting method following query by example (QBE) setting for handwritten documents. It consists of four key processes: pre-processing, vertical zone division, feature extraction, and feature matching. The pre-processing step deals with the noise found in the word images, and the skewness of the handwritings caused by the varied writing styles of the individuals. Next, the vertical zone division splits the word image into several zones. The number of vertical zones is guided by the number of letters in the query word image. To obtain this information (i.e., number of letters in a query word image) during experimentation, we use the text encoding of the query word image. The user provides the information to the system. The feature extraction process involves the use of the Hough transform. The last step is feature matching, which first compares the features extracted from the word images and then generates a similarity score. The performance of this algorithm has been tested on three publicly available datasets: IAM, QUWI, and ICDAR KWS 2015. It is noticed that the proposed method outperforms state-of-the-art learning-free KWS methods considered here for comparison while evaluated on the present datasets. We also evaluate the performance of the present KWS model using state-of-the-art deep features and it is found that the features used in the present work perform better than the deep features extracted using InceptionV3, VGG19, and DenseNet121 models.

show abstract

“…The algorithm can filter the background, which contains text image of printed documents. Vikas and Ragot [16] designed a very simple technique based on FAST key points to extract texts from document images. The image is divided into blocks and the point density of each block is computed.…”

Section: A Layout Segmentationmentioning

confidence: 99%

Segmentation and Recognition for Historical Tibetan Document Images

Long

Duan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

As a shining pearl in traditional Tibetan culture, historical Tibetan documents have received extensive attention from historians, linguists and Buddhist scholars. These documents are converted into digital form using Tibetan document segmentation and recognition methods. The document digitization is of great significance for the research, protection and inheritance of Tibetan history. This paper proposes an overall segmentation and recognition framework for historical Tibetan document images. Firstly, the historical Tibetan document image is preprocessed to correct imbalanced illumination, tilt and noises, and is further transformed into the binarized image. Secondly, we propose a layout segmentation method based on block projection to segment Tibetan document images into texts, lines and frames. Thirdly, in order to solve the problems of touching strokes between text-lines and curvilinear text-lines, we present a text-line segmentation method based on graph model for historical Tibetan text-line segmentation. Lastly, we present a touching segmentation method to segment touching Tibetan character string, and then recognize Tibetan characters. Experimental results show our proposed methods on layout segmentation, text-line segmentation and touching character string segmentation, achieve the satisfactory performance. The proposed methods can also be applied to other fonts in Tibetan font family.

show abstract

Text Extraction in Document Images: Highlight on Using Corner Points

Cited by 19 publications

References 30 publications

Image Purification Technique for Myanmar OCR Applying Skew Angle Detection and Free Skew

Image Purification Technique for Myanmar OCR Applying Skew Angle Detection and Free Skew

Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Segmentation and Recognition for Historical Tibetan Document Images

Contact Info

Product

Resources

About