Textual Content Retrieval from Filled-in Form Images

Ghosh, Soulib; Bhattacharya, Rajdeep; Majhi, Sandipan; Bhowmik, Showmik; Malakar, Samir; Sarkar, Ram

doi:10.1007/978-981-13-9361-7_3

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…ρ r and θ values determine the row count (say, r) and column count (say, c) of H along ρ and θ axes, respectively. The r value is estimated by Equations ( 14) and (15).…”

Section: Feature Extractionmentioning

confidence: 99%

“…The use of the page segmentation technique generates different inputs (i.e., text line or word) for the system, and accordingly, a segmentation-based KWS technique classified into two categories: (i) text line based technique, which locates a query word within pre-segmented text lines [ 10 , 11 ]; and (ii) word-based technique, which assumes the document image is already segmented into word images and, therefore, only focuses on matching the query word image with the target words [ 12 , 13 , 14 ]. On the other hand, methods that follow a segmentation-free approach do not require any prior page segmentation technique, i.e., these methods do not possess any knowledge about the document structure or any such similar templates [ 15 , 16 ]. The segmentation-based technique is a better option between these two approaches if we consider computational cost and compare the performances of these two categories of works in the literature.…”

Section: Introductionmentioning

confidence: 99%

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

Kundu

Malakar²,

Geem

et al. 2021

Sensors

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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.

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“…ρ r and θ values determine the row count (say, r) and column count (say, c) of H along ρ and θ axes, respectively. The r value is estimated by Equations ( 14) and (15).…”

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kundu

Malakar²,

Geem

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, CC-based classification is better where each component is handled individually to classify it as text or non-text (Mondal et al 2020). For example, Ghosh et al (2019) have come up with a threshold-based approach, which considers various shape-based features for different categories of commonly used non-texts to classify the components. In another work, Sah et al (2017) have used a modified version of a histogram of oriented gradients (HOG) feature descriptor followed by a multi-layer perceptron (MLP) classifier to perform the same.…”

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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Fuzzy-Based Pseudo Segmentation Approach for Handwritten Word Recognition Using a Sequence to Sequence Model with Attention

Bhattacharya

Malakar²,

Schwenker

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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Textual Content Retrieval from Filled-in Form Images

Cited by 9 publications

References 10 publications

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

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

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

Fuzzy-Based Pseudo Segmentation Approach for Handwritten Word Recognition Using a Sequence to Sequence Model with Attention

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