Word Spotting Using Convolutional Siamese Network

Barakat, Berat Kurar; Alasam, Reem; El-Sana, Jihad

doi:10.1109/das.2018.67

Cited by 17 publications

(11 citation statements)

References 26 publications

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“…This can be because only one feature extraction method is used in [20] whereas the proposed method has two stages and different feature extraction techniques. Though the learning‐based methods [21] has used a CNN architecture for training a model, the results are lower when compared to the proposed method. The dataset used in [21] is George Washington dataset which has approximately 5000 segmented word images overall with 943 classes.…”

Section: Discussionmentioning

confidence: 99%

Learning‐free handwritten word spotting method for historical handwritten documents

Hassen

Subramanian

Al-Madeed

2021

IET Image Processing

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Word spotting on degraded and noisy historical documents can become a challenging task considering the computational time and memory usage required to scan the entire document image. This paper proposes a new effective technique for multi-language word spotting using a two different feature extraction techniques, Histogram of Oriented Gradients (HOG) and Speeded Up Robust Features (SURF) features. First, regions of interest (ROIs) are extracted using a cross-correlation measure, and the extracted ROIs are reranked using feature extraction and matching methods. The algorithm handles two types of scenarios: Segmentation-based and segmentation-free. It also facilitates the search for words that occur once as well as multiple times in the image. Evaluations were conducted on the George Washington and HADARA datasets using a standard evaluation method. The proposed methodology shows improved performance over contemporary technologies currently being used in the word spotting research field.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Discussionmentioning

confidence: 99%

Learning‐free handwritten word spotting method for historical handwritten documents

Hassen

Subramanian

Al-Madeed

2021

IET Image Processing

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“…The authors in [19] have considered the recent TPP-PHOCNet for comparison between residual networks and LeNet architectures. A convolutional Siamese network consisting of two convolutional networks to study the similarity between two word images has been proposed by the authors in [20]. The VML-HD dataset has been used in [20] for evaluation purposes.…”

Section: Related Workmentioning

confidence: 99%

WSNet – Convolutional Neural Networkbased Word Spotting for Arabic and English Handwritten Documents

Hassen¹,

Subramanian²,

Al-Maadeed³

et al. 2022

TEM Journal

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This paper proposes a new convolutional neural network architecture to tackle the problem of word spotting in handwritten documents. A Deep learning approach using a novel Convolutional Neural Network is developed for the recognition of the words in historical handwritten documents. This includes a pre-processing step to re-size all the images to a fixed size. These images are then fed to the CNN for training. The proposed network shows promising results for both Arabic and English and both modern and historical documents. Four datasets – IFN/ENIT, Visual Media Lab – Historical Documents (VML-HD), George Washington and IAM datasets – have been used for evaluation. It is observed that the mean average precision for the George Washington dataset is 99.6%, outperforming other state-of-the-art methods. Historical documents in Arabic are known for being complex to work with; this model shows good results for the Arabic datasets, as well. This indicates that the architecture is also able to generalize well to other languages.

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“…Inspired by this work [ 13 ], Sudholt and Fink [ 14 ] exploit the concept in deep convolutional neural networks (CNNs), which is known as PHOCNet. Recently, another deep learning-based KWS technique was proposed by Barakat et al [ 38 ] where a Siamese Network was used.…”

Section: Previous Workmentioning

confidence: 99%

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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Word Spotting Using Convolutional Siamese Network

Cited by 17 publications

References 26 publications

Learning‐free handwritten word spotting method for historical handwritten documents

Learning‐free handwritten word spotting method for historical handwritten documents

WSNet – Convolutional Neural Networkbased Word Spotting for Arabic and English Handwritten Documents

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

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