Word Segmentation by Component Tracing and Association (CTA)  Technique

Omayio, Enock Osoro; Indu, S.; Panda, Jeebananda

doi:10.36909/jer.15207

Cited by 4 publications

(2 citation statements)

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“…For instance, there might be noise, twisted words, missing or partial letters in words, words that are not available as straight text lines, etc. Some examples given in this topic of identifying words in a text line are [59] for offline-handwritten and [60] for offline-printed.…”

Section: Text Line Levelmentioning

confidence: 99%

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: Text Line Levelmentioning

confidence: 99%

Construction of Alphabetic Character Recognition Systems: A Review

Majeed

Nariman

2023

UHD J SCI TECH

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“…The text segmentation stage represents a fundamental preprocessing step in the context of most handwriting recognition systems. The segmentation task can include character segmentation [2], word segmentation [3], and line segmenta-tion serving as the primary step when working with complex documents provided as input. The overall performance of a handwritten recognition system strongly relies on the results of the text line detection process.…”

Section: Introductionmentioning

confidence: 99%

An overview of statistical and neural-based line segmentation methods for offline handwriting recognition task

Yakovchuk,

Rogoza

2024

TAPR

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The object of the research is the line segmentation task. To recognize the handwritten text from the documents in image format offline handwriting recognition technology is used. The text recognizer module accepts input as separate lines, so one of the important preprocessing steps is the detection and splitting of all handwritten text into distinct lines. In this paper, the handwritten text line segmentation task, its requirements, problems, and challenges are examined. Two main approaches for this task that are used in modern recognition systems are reviewed. These approaches are statistical projection-based methods and neural-based methods. Multiple works and research papers for each type of approach are reviewed analyzing their strengths and weaknesses considering the described tasks, constraints, and input data peculiarities. Overall acquired results are formed in a single table for comparison. Based on the latest works that utilize deep neural networks the new possibilities of using these methods in recognition systems are described that were unavailable with traditional statistical segmentation approaches. The constructive conclusions are made based on the review, describing the main pros and cons of these two approaches for the line segmentation task. These results can be further used for the correct selection of suitable methods in handwriting recognition systems to improve their performance and quality, and for further research in this area.

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