Evidence map›Paper›PMID 39145239›Full record

ArticlePeerJ. Computer science2024

Recognition of inscribed cursive Pashtu numeral through optimized deep learning.

Sibtain Syed, Khalil Khan, Maqbool Khan, Rehan Ullah Khan, Abdulrahman Aloraini

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sibtain SyedDepartment of IT & CS, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, KP, Pakistan.
Khalil KhanDepartment of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
Maqbool KhanDepartment of IT & CS, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, KP, Pakistan.
Rehan Ullah KhanDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.
Abdulrahman AlorainiDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pashtu is one of the most widely spoken languages in south-east Asia. Pashtu Numerics recognition poses challenges due to its cursive nature. Despite this, employing a machine learning-based optical character recognition (OCR) model can be an effective way to tackle this issue. The main aim of the study is to propose an optimized machine learning model which can efficiently identify Pashtu numerics from 0-9. The methodology includes data organizing into different directories each representing labels. After that, the data is preprocessed

Indexed as

Convolution neural networksLong short term memoryOptical character recognitionPashtu scriptPattern recognition

Identifiers

PMID39145239
PMCPMC11323096

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.