Special Issue "Recent Advances in Handwritten Text Recognition"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 6670

Special Issue Editors

Dr. Carlos-D. Martínez Hinarejos
E-Mail Website
Guest Editor
Pattern Recognition and Human Language Technologies research center – Universitat Politècnica de València, Valencia, Spain
Interests: handwritten text processing; speech processing; multimodality; dialogue systems; text classification
Dr. Byron Leite Dantas Bezerra
E-Mail Website
Guest Editor
Universidade de Pernambuco, Recife, Brazil
Interests: printed and handwritten document recognition and processing; pattern recognition; digital image processing; computer vision; biometrics
Dr. Donato Impedovo
E-Mail Website
Guest Editor
Department of Computer Science, University of Bari, Bari, Italy
Interests: biometrics; automatic signature verification; artificial intelligence; pattern recognition, signal processing
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Special Issue Information

Dear Colleagues,

In the last two decades, the progress in handwritten text processing has allowed us to increment the performance of the systems devoted to the transcription, indexing, and semantic interpretation of handwritten text documents. The progress has covered all the steps in this field, from image capture and enhancement to information extraction based on the contents of the documents. As a result, lots of data that were difficult to process from the natural language perspective have became available for specialized researchers and the general public, especially when dealing with the contents of historical archives.

The aim of this Special Issue is to attract world-leading researchers in the handwritten text processing field in an effort to show recent progress in the area, including the development of new paradigms and models and new applications of the state-of-the-art technology.

Dr. Carlos-D. Martínez Hinarejos
Dr. Byron Leite Dantas Bezerra
Dr. Donato Impedovo
Guest Editors

Manuscript Submission Information

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Keywords

  • Handwritten Document Image Processing
  • Handwritten Document Layout Analysis
  • Handwritten Character and Text Recognition
  • Indexing and Semantic Information Extraction from Handwritten Documents
  • Historical Handwritten Document Processing
  • Handwritten Document Forensics and Writer Identification
  • Handwritten Databases and Digital Libraries
  • Multimedia Documents
  • Machine Learning for Handwritten Text Processing
  • Applications

Published Papers (5 papers)

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Research

Article
Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples
Appl. Sci. 2021, 11(11), 4894; https://doi.org/10.3390/app11114894 - 26 May 2021
Cited by 1 | Viewed by 854
Abstract
The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task [...] Read more.
The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation. Full article
(This article belongs to the Special Issue Recent Advances in Handwritten Text Recognition)
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Article
Learning-Free Text Line Segmentation for Historical Handwritten Documents
Appl. Sci. 2020, 10(22), 8276; https://doi.org/10.3390/app10228276 - 22 Nov 2020
Cited by 4 | Viewed by 852
Abstract
We present a learning-free method for text line segmentation of historical handwritten document images. This method relies on automatic scale selection together with second derivative of anisotropic Gaussian filters to detect the blob lines that strike through the text lines. Detected blob lines [...] Read more.
We present a learning-free method for text line segmentation of historical handwritten document images. This method relies on automatic scale selection together with second derivative of anisotropic Gaussian filters to detect the blob lines that strike through the text lines. Detected blob lines guide an energy minimization procedure to extract the text lines. Historical handwritten documents contain noise, heterogeneous text line heights, skews and touching characters among text lines. Automatic scale selection allows for automatic adaption to the heterogeneous nature of handwritten text lines in case the character height range is correctly estimated. In the extraction phase, the method can accurately split the touching characters among the text lines. We provide results investigating various settings and compare the model with recent learning-free and learning-based methods on the cBAD competition dataset. Full article
(This article belongs to the Special Issue Recent Advances in Handwritten Text Recognition)
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Article
Towards the Natural Language Processing as Spelling Correction for Offline Handwritten Text Recognition Systems
Appl. Sci. 2020, 10(21), 7711; https://doi.org/10.3390/app10217711 - 31 Oct 2020
Cited by 6 | Viewed by 1832
Abstract
The increasing portability of physical manuscripts to the digital environment makes it common for systems to offer automatic mechanisms for offline Handwritten Text Recognition (HTR). However, several scenarios and writing variations bring challenges in recognition accuracy, and, to minimize this problem, optical models [...] Read more.
The increasing portability of physical manuscripts to the digital environment makes it common for systems to offer automatic mechanisms for offline Handwritten Text Recognition (HTR). However, several scenarios and writing variations bring challenges in recognition accuracy, and, to minimize this problem, optical models can be used with language models to assist in decoding text. Thus, with the aim of improving results, dictionaries of characters and words are generated from the dataset and linguistic restrictions are created in the recognition process. In this way, this work proposes the use of spelling correction techniques for text post-processing to achieve better results and eliminate the linguistic dependence between the optical model and the decoding stage. In addition, an encoder–decoder neural network architecture in conjunction with a training methodology are developed and presented to achieve the goal of spelling correction. To demonstrate the effectiveness of this new approach, we conducted an experiment on five datasets of text lines, widely known in the field of HTR, three state-of-the-art Optical Models for text recognition and eight spelling correction techniques, among traditional statistics and current approaches of neural networks in the field of Natural Language Processing (NLP). Finally, our proposed spelling correction model is analyzed statistically through HTR system metrics, reaching an average sentence correction of 54% higher than the state-of-the-art method of decoding in the tested datasets. Full article
(This article belongs to the Special Issue Recent Advances in Handwritten Text Recognition)
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Article
Line-segment Feature Analysis Algorithm Using Input Dimensionality Reduction for Handwritten Text Recognition
Appl. Sci. 2020, 10(19), 6904; https://doi.org/10.3390/app10196904 - 01 Oct 2020
Cited by 2 | Viewed by 1085
Abstract
Recently, demand for handwriting recognition, such as automation of mail sorting, license plate recognition, and electronic memo pads, has exponentially increased in various industrial fields. In addition, in the image recognition field, methods using artificial convolutional neural networks, which show outstanding performance, have [...] Read more.
Recently, demand for handwriting recognition, such as automation of mail sorting, license plate recognition, and electronic memo pads, has exponentially increased in various industrial fields. In addition, in the image recognition field, methods using artificial convolutional neural networks, which show outstanding performance, have been applied to handwriting recognition. However, owing to the diversity of recognition application fields, the number of dimensions in the learning and reasoning processes is increasing. To solve this problem, a principal component analysis (PCA) technique is used for dimensionality reduction. However, PCA is likely to increase the accuracy loss due to data compression. Therefore, in this paper, we propose a line-segment feature analysis (LFA) algorithm for input dimensionality reduction in handwritten text recognition. This proposed algorithm extracts the line segment information, constituting the image of input data, and assigns a unique value to each segment using 3 × 3 and 5 × 5 filters. Using the unique values to identify the number of line segments and adding them up, a 1-D vector with a size of 512 is created. This vector is used as input to machine-learning. For the performance evaluation of the method, the Extending Modified National Institute of Standards and Technology (EMNIST) database was used. In the evaluation, PCA showed 96.6% and 93.86% accuracy with k-nearest neighbors (KNN) and support vector machine (SVM), respectively, while LFA showed 97.5% and 98.9% accuracy with KNN and SVM, respectively. Full article
(This article belongs to the Special Issue Recent Advances in Handwritten Text Recognition)
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Article
Handwritten Digit Recognition: Hyperparameters-Based Analysis
Appl. Sci. 2020, 10(17), 5988; https://doi.org/10.3390/app10175988 - 29 Aug 2020
Cited by 4 | Viewed by 1306
Abstract
Neural networks have several useful applications in machine learning. However, benefiting from the neural-network architecture can be tricky in some instances due to the large number of parameters that can influence performance. In general, given a particular dataset, a data scientist cannot do [...] Read more.
Neural networks have several useful applications in machine learning. However, benefiting from the neural-network architecture can be tricky in some instances due to the large number of parameters that can influence performance. In general, given a particular dataset, a data scientist cannot do much to improve the efficiency of the model. However, by tuning certain hyperparameters, the model’s accuracy and time of execution can be improved. Hence, it is of utmost importance to select the optimal values of hyperparameters. Choosing the optimal values of hyperparameters requires experience and mastery of the machine learning paradigm. In this paper, neural network-based architectures are tested based on altering the values of hyperparameters for handwritten-based digit recognition. Various neural network-based models are used to analyze different aspects of the same, primarily accuracy based on hyperparameter values. The extensive experimentation setup in this article should, therefore, provide the most accurate and time-efficient solution models. Such an evaluation will help in selecting the optimized values of hyperparameters for similar tasks. Full article
(This article belongs to the Special Issue Recent Advances in Handwritten Text Recognition)
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