Offline script recognition from handwritten and printed multilingual documents: a survey

Sinwar, Deepak; Dhaka, Vijaypal Singh; Pradhan, Nitesh; Pandey, Saumya

doi:10.1007/s10032-021-00365-5

Cited by 14 publications

(9 citation statements)

References 132 publications

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“…Furthermore, in the context of CR, the investigations of the script character characteristics and structural properties, the script-written system has been classified under six classes. Different classes may contain the same language scripts [8], [9], [10]. Fig.…”

Section: Script Writing Systemmentioning

confidence: 99%

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Construction of Alphabetic Character Recognition Systems: A Review

Majeed

Nariman

2023

UHD J SCI TECH

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Character recognition (CR) systems were attracted by a massive number of authors’ interest in this field, and lot of research has been proposed, developed, and published in this regard with different algorithms and techniques due to the great interest and demand of raising the accuracy of the recognition rate and the reliability of the presented system. This work is proposed to provide a guideline for CR system construction to afford a clear view to the authors on building their systems. All the required phases and steps have been listed and clarified within sections and subsections along with detailed graphs and tables beside the possibilities of techniques and algorithms that might be used, developed, or merged to create a high-performance recognition system. This guideline also could be useful for readers interested in this field by helping them extract the information from such papers easily and efficiently to reach the main structure along with the differences between the systems. In addition, this work recommends to researchers in this field to comprehend a specified categorical table in their work to provide readers with the main structure of their work that shows the proposed system’s structural layout and enables them to easily find the information and interests.

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Section: Script Writing Systemmentioning

confidence: 99%

“…However, from the identification/ detection point of view, it means that the identification and detection of text lines are working. Further details about these processes can be found in [10], [63].…”

Section: Character Levelmentioning

confidence: 99%

Construction of Alphabetic Character Recognition Systems: A Review

Majeed

Nariman

2023

UHD J SCI TECH

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“…After performed an in-depth review of several research articles including survey articles [2]- [4], we conclude that the entire process of alphabetic handwriting recognition could be classified under some separated classification types based on several factors as below.…”

Section: Introductionmentioning

confidence: 99%

“…The scriptwriting system type defines the selected language to be recognized in the proposed system. The languages which are in use today throughout the world have been defined under several different systems, more details can be found in Sinwar et al [2], Ghosh and Shivaprasad [3], Pal [4], Ubul et al [5].…”

Section: Introductionmentioning

confidence: 99%

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Offline Handwritten English Alphabet Recognition (OHEAR)

Majeed

Nariman

2022

UHD J SCI TECH

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In most pattern recognition models, the accuracy of the recognition plays a major role in the efficiency of those models. The feature extraction phase aims to sum up most of the details and findings contained in those patterns to be informational and non-redundant in a way that is sufficient to fen to the used classifier of that model and facilitate the subsequent learning process. This work proposes a highly accurate offline handwritten English alphabet (OHEAR) model for recognizing through efficiently extracting the most informative features from constructed self-collected dataset through three main phases: Pre-processing, features extraction, and classification. The features extraction is the core phase of OHEAR based on combining both statistical and structural features of the certain alphabet sample image. In fact, four feature extraction portions, this work has utilized, are tracking adjoin pixels, chain of redundancy, scaled-occupancy-rate chain, and density feature. The feature set of 27 elements is constructed to be provided to the multi-class support vector machine (MSVM) for the process of classification. The OHEAR resultant revealed an accuracy recognition of 98.4%.

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A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions

Ramo

Silvestre

2023

Communications in Computer and Information Science

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The Transformer architecture is shown to provide a powerful framework as an end-to-end model for building expression trees from online handwritten gestures corresponding to glyph strokes. In particular, the attention mechanism was successfully used to encode, learn and enforce the underlying syntax of expressions creating latent representations that are correctly decoded to the exact mathematical expression tree, providing robustness to ablated inputs and unseen glyphs. For the first time, the encoder is fed with spatio-temporal data tokens potentially forming an infinitely large vocabulary, which finds applications beyond that of online gesture recognition. A new supervised dataset of online handwriting gestures is provided for training models on generic handwriting recognition tasks and a new metric is proposed for the evaluation of the syntactic correctness of the output expression trees. A small Transformer model suitable for edge inference was successfully trained to an average normalised Levenshtein accuracy of 94%, resulting in valid postfix RPN tree representation for 94% of predictions.

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Offline script recognition from handwritten and printed multilingual documents: a survey

Cited by 14 publications

References 132 publications

Construction of Alphabetic Character Recognition Systems: A Review

Construction of Alphabetic Character Recognition Systems: A Review

Offline Handwritten English Alphabet Recognition (OHEAR)

A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions

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