A Review of Deep Learning Techniques in Document Image Word Spotting

Kumari, Lalita; Sharma, Anuj

doi:10.1007/s11831-021-09605-7

Cited by 8 publications

(3 citation statements)

References 115 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the training time, they estimate the certainty quotients among the query and target words, which was later used for word spotting purposes. Recently, Kumari and Sharma [48] described many such deep learning-based methods and their effect on the KWS.…”

Section: Learning-based Methodsmentioning

confidence: 99%

Z-Transform-Based Profile Matching to Develop a Learning-Free Keyword Spotting Method for Handwritten Document Images

Banerjee

Bhowal²,

Malakar³

et al. 2022

Int J Comput Intell Syst

View full text Add to dashboard Cite

For easy accessibility of the information from the digitized document images, optical character recognition (OCR)-based software can be used. But in the case of handwritten documents, the performance of the state-of-the-art OCR systems is not satisfactory owing to the complexity of the unconstrained handwriting. Hence, research affinity comes up with an alternative solution for this problem called keyword spotting (KWS) which is much more practical than an OCR-based solution. This work proposes a novel learning-free KWS method that can be applied to a heterogeneous collection of handwritten documents. In this work, we introduce a new way of profile matching to compare the query word profiles (i.e., both upper and lower) with the target words’ profiles. At first, both query and target words are binarized, and then two profiles from each such word are generated. Next, we formulate rules to filter out the irrelevant words concerning the query word and obtain the probable candidate query (i.e., target) words. Then we compare the profiles of the query and candidate query words in the Z-transform domain using the condition of resonance for the damped oscillator. However, before the match, we perform an affine transformation on the Bezier curve representation of the profiles of the candidate query words to reduce the effects like scaling, rotation, and shearing which might occur due to the variant writing styles of individuals. The proposed method achieves satisfactory performance compared to state-of-the-art learning-free methods when applied to four publicly available standard datasets namely ICFHR 2014 H-KWS competition Modern, IAM, ICFHR 2016 H-KWS competition Botany and ICFHR 2016 H-KWS competition Konzilsprotokolle datasets.

show abstract

Section: Learning-based Methodsmentioning

confidence: 99%

Z-Transform-Based Profile Matching to Develop a Learning-Free Keyword Spotting Method for Handwritten Document Images

Banerjee

Bhowal²,

Malakar³

et al. 2022

Int J Comput Intell Syst

View full text Add to dashboard Cite

show abstract

“…The HTR problem is complex enough to be widely studied by computer vision researchers across the community. The HTR system is of two types as online and offline [1] [2]. In an online recognition system, the pen movements are captured based upon time series.…”

Section: Introductionmentioning

confidence: 99%

LexiconNet: An End-to-End Handwritten Paragraph Text Recognition System

Kumari¹,

Singh²,

Rathore³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Historical documents present in the form of libraries needs to be digitised. The recognition of these unconstrained cursive handwritten documents is a challenging task. In the present work, neural network based classifier is used. The recognition of scanned document images which are easy to train on neural network based systems is usually done by a two step approach: segmentation followed by recognition. This approach has several shortcomings, which includes identification of text regions, layout diversity analysis present within pages and ground truth segmentation. These processes are prone to errors that create bottleneck in the recognition accuracies. Thus in this study, an end-to-end paragraph recognition system is presented with internal line segmentation and lexicon decoder as post processing step, which is free from those errors. We named our model as LexiconNet. In LexiconNet, given a paragraph image a combination of convolution and depth-wise separable convolutional modules generates the two dimension feature map of the image. The attention module is responsible for internal line segmentation that consequently processing a page in a line by line manner. At decoding step, we have added connectionist temporal classification based word beam search decoder as a post processing step. Our approach reports state-of-the-art results on standard datasets. The reported character error rate is 3.24% on IAM dataset with 27.19% improvement, 1.13% on RIMES with 40.83% improvement and 2.43% on READ-16 dataset with 32.31% improvement from existing literature and the word error rate is 8.29% on IAM dataset with 43.02% improvement, 2.94% on RIMES dataset with 56.25% improvement and 7.35% on READ-2016 dataset with 47.27% improvement from the existing results. The character error rate and word error rate reported in this work surpasses the results reported in literature.

show abstract

“…Firstly, for handwritten text, it is not always feasible to do word segmentation due to their proximity or partially overlapping locations. In addition to this, for densely written handwritten text, it is complex to detect large number of words and at last there may be scenarios where a word is not separated by a space which is a word separator considered by most of the linguistic systems [36]. Thus this study focuses on line level recognition architecture to make the system as generic as possible.…”

Section: Introductionmentioning

confidence: 99%

Lexicon and attention based handwritten text recognition system

Kumari

Singh²,

Rathore³

et al. 2022

MG&V

Self Cite

View full text Add to dashboard Cite

The handwritten text recognition problem is widely studied by the researchers of computer vision community due to its scope of improvement and applicability to daily lives. It is a sub-domain of pattern recognition. Due to advancement of computational power of computers since last few decades neural networks based systems heavily contributed towards providing the state-of-the-art handwritten text recognizers. In the same direction, we have taken two state-of-the art neural networks systems and merged the attention mechanism with it. The attention technique has been widely used in the domain of neural machine translations and automatic speech recognition and now is being implemented in text recognition domain. In this study, we are able to achieve 4.15% character error rate and 9.72% word error rate on IAM dataset, 7.07% character error rate and 16.14% word error rate on GW dataset after merging the attention and word beam search decoder with existing Flor et al. architecture. To analyse further, we have also used system similar to Shi et al. neural network system with greedy decoder and observed 23.27% improvement in character error rate from the base model.

show abstract

A Review of Deep Learning Techniques in Document Image Word Spotting

Cited by 8 publications

References 115 publications

Z-Transform-Based Profile Matching to Develop a Learning-Free Keyword Spotting Method for Handwritten Document Images

Z-Transform-Based Profile Matching to Develop a Learning-Free Keyword Spotting Method for Handwritten Document Images

LexiconNet: An End-to-End Handwritten Paragraph Text Recognition System

Lexicon and attention based handwritten text recognition system

Contact Info

Product

Resources

About