An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition

Saritha, B.; Hemanth, S. V.

doi:10.48550/arxiv.1001.5334

Cited by 4 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fortunately, HTR systems have incredibly improved since utilizing the Hidden Markov Model (HMM) for text recognition and handcrafted features [6][7][8]. However, the recognition results of HMMs are still poor due to some drawbacks in the model, such as memorylessness [9] and the manual feature selection process.…”

Section: Introductionmentioning

confidence: 99%

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

AlKendi,

Gechter,

Heyberger

et al. 2024

J. Imaging

View full text Add to dashboard Cite

Handwritten Text Recognition (HTR) is essential for digitizing historical documents in different kinds of archives. In this study, we introduce a hybrid form archive written in French: the Belfort civil registers of births. The digitization of these historical documents is challenging due to their unique characteristics such as writing style variations, overlapped characters and words, and marginal annotations. The objective of this survey paper is to summarize research on handwritten text documents and provide research directions toward effectively transcribing this French dataset. To achieve this goal, we presented a brief survey of several modern and historical HTR offline systems of different international languages, and the top state-of-the-art contributions reported of the French language specifically. The survey classifies the HTR systems based on techniques employed, datasets used, publication years, and the level of recognition. Furthermore, an analysis of the systems’ accuracies is presented, highlighting the best-performing approach. We have also showcased the performance of some HTR commercial systems. In addition, this paper presents a summarization of the HTR datasets that publicly available, especially those identified as benchmark datasets in the International Conference on Document Analysis and Recognition (ICDAR) and the International Conference on Frontiers in Handwriting Recognition (ICFHR) competitions. This paper, therefore, presents updated state-of-the-art research in HTR and highlights new directions in the research field.

show abstract

Section: Introductionmentioning

confidence: 99%

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

AlKendi,

Gechter,

Heyberger

et al. 2024

J. Imaging

View full text Add to dashboard Cite

show abstract

“…Fortunately, HTR systems have evolved considerably since the use of the Hidden Markov Model (HMM) for text recognition [2,[9][10][11]. Currently, with the use of Deep Neural Networks (Deep Learning), it is possible to more assertively perform the recognition process at different levels of text segmentation that is, character [12], word [13,14], line [15] and even the paragraph [16] levels.…”

Section: Introductionmentioning

confidence: 99%

Towards the Natural Language Processing as Spelling Correction for Offline Handwritten Text Recognition Systems

2020

View full text Add to dashboard Cite

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.

show abstract

Structural offline handwriting character recognition using levenshtein distance

Putra

Supriana

2015

2015 International Conference on Electrical Engineering and Informatics (ICEEI)

View full text Add to dashboard Cite

An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition

Cited by 4 publications

References 27 publications

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

Advancements and Challenges in Handwritten Text Recognition: A Comprehensive Survey

Towards the Natural Language Processing as Spelling Correction for Offline Handwritten Text Recognition Systems

Structural offline handwriting character recognition using levenshtein distance

Contact Info

Product

Resources

About