Modified Segmentation Algorithm for Recognition of Older Geez Scripts
Written on Vellum
- URL: http://arxiv.org/abs/2006.00465v1
- Date: Sun, 31 May 2020 08:16:27 GMT
- Title: Modified Segmentation Algorithm for Recognition of Older Geez Scripts
Written on Vellum
- Authors: Girma Negashe, Adane Mamuye
- Abstract summary: Recognition of handwritten document aims at transforming document images into a machine understandable format.
Handwritten document recognition is the most challenging area in the field of pattern recognition.
In this study, we introduced a modified segmentation approach to recognize older Geez scripts.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recognition of handwritten document aims at transforming document images into
a machine understandable format. Handwritten document recognition is the most
challenging area in the field of pattern recognition. It becomes more complex
when a document was written on vellum before hundreds of years, like older Geez
scripts. In this study, we introduced a modified segmentation approach to
recognize older Geez scripts. We used adaptive filtering for noise reduction,
Isodata iterative global thresholding for document image binarization, modified
bounding box projection to segment distinct strokes between Geez characters,
numbers, and punctuation marks. SVM multiclass classifier scored 79.32%
recognition accuracy with the modified segmentation algorithm.
Related papers
- Learning Robust Named Entity Recognizers From Noisy Data With Retrieval Augmentation [67.89838237013078]
Named entity recognition (NER) models often struggle with noisy inputs.
We propose a more realistic setting in which only noisy text and its NER labels are available.
We employ a multi-view training framework that improves robust NER without retrieving text during inference.
arXiv Detail & Related papers (2024-07-26T07:30:41Z) - Leveraging Semantic Segmentation Masks with Embeddings for Fine-Grained Form Classification [0.0]
Efficient categorization of historical documents is crucial for fields such as genealogy, legal research and historical scholarship.
We propose a representational learning strategy that integrates deep learning models such as ResNet, masked Image Transformer (Di), and embedding segmentation.
arXiv Detail & Related papers (2024-05-23T04:28:50Z) - GatedLexiconNet: A Comprehensive End-to-End Handwritten Paragraph Text Recognition System [3.9527064697847005]
We present an end-to-end paragraph recognition system that incorporates internal line segmentation and convolutional layers based encoder.
This study reported character error rates of 2.27% on IAM, 0.9% on RIMES, and 2.13% on READ-16, and word error rates of 5.73% on READ-2016 datasets.
arXiv Detail & Related papers (2024-04-22T10:19:16Z) - Efficiently Leveraging Linguistic Priors for Scene Text Spotting [63.22351047545888]
This paper proposes a method that leverages linguistic knowledge from a large text corpus to replace the traditional one-hot encoding used in auto-regressive scene text spotting and recognition models.
We generate text distributions that align well with scene text datasets, removing the need for in-domain fine-tuning.
Experimental results show that our method not only improves recognition accuracy but also enables more accurate localization of words.
arXiv Detail & Related papers (2024-02-27T01:57:09Z) - Character Queries: A Transformer-based Approach to On-Line Handwritten
Character Segmentation [4.128716153761773]
We focus on the scenario where the transcription is known beforehand, in which case the character segmentation becomes an assignment problem.
Inspired by the $k$-means clustering algorithm, we view it from the perspective of cluster assignment and present a Transformer-based architecture.
In order to assess the quality of our approach, we create character segmentation ground truths for two popular on-line handwriting datasets.
arXiv Detail & Related papers (2023-09-06T15:19:04Z) - DocMAE: Document Image Rectification via Self-supervised Representation
Learning [144.44748607192147]
We present DocMAE, a novel self-supervised framework for document image rectification.
We first mask random patches of the background-excluded document images and then reconstruct the missing pixels.
With such a self-supervised learning approach, the network is encouraged to learn the intrinsic structure of deformed documents.
arXiv Detail & Related papers (2023-04-20T14:27:15Z) - Unified Mask Embedding and Correspondence Learning for Self-Supervised
Video Segmentation [76.40565872257709]
We develop a unified framework which simultaneously models cross-frame dense correspondence for locally discriminative feature learning.
It is able to directly learn to perform mask-guided sequential segmentation from unlabeled videos.
Our algorithm sets state-of-the-arts on two standard benchmarks (i.e., DAVIS17 and YouTube-VOS)
arXiv Detail & Related papers (2023-03-17T16:23:36Z) - Language-driven Semantic Segmentation [88.21498323896475]
We present LSeg, a novel model for language-driven semantic image segmentation.
We use a text encoder to compute embeddings of descriptive input labels.
The encoder is trained with a contrastive objective to align pixel embeddings to the text embedding of the corresponding semantic class.
arXiv Detail & Related papers (2022-01-10T18:59:10Z) - End-to-End Approach for Recognition of Historical Digit Strings [2.0754848504005583]
We propose an end-to-end segmentation-free deep learning approach to handle challenging ancient handwriting style of dates present in the ARDIS dataset (4-digits long strings)
We show that with slight modifications in the VGG-16 deep model, the framework can achieve a recognition rate of 93.2%, resulting in a feasible solution free of methods, segmentation, and fusion methods.
arXiv Detail & Related papers (2021-04-28T09:39:29Z) - Word Segmentation from Unconstrained Handwritten Bangla Document Images
using Distance Transform [34.89370782262938]
This paper addresses the automatic segmentation of text words directly from unconstrained Bangla handwritten document images.
The popular Distance algorithm is applied for locating the outer boundary of the word images.
The proposed technique is tested on 50 random images taken from CMATERdb database.
arXiv Detail & Related papers (2020-09-17T03:14:27Z) - TextScanner: Reading Characters in Order for Robust Scene Text
Recognition [60.04267660533966]
TextScanner is an alternative approach for scene text recognition.
It generates pixel-wise, multi-channel segmentation maps for character class, position and order.
It also adopts RNN for context modeling and performs paralleled prediction for character position and class.
arXiv Detail & Related papers (2019-12-28T07:52:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.