A System for Bangla Handwritten Numeral Recognition

International audienceThis paper deals with a recognition system for unconstrained off-line Bangla handwritten numerals. To take care of variability involved in the writing style of different individuals, a robust scheme is presented here. The scheme is mainly based on new features obtained from the concept of water overflow from the reservoir as well as topological and structural features of the numerals. The proposed scheme is tested on data collected from different individuals of various background and we obtained an overall recognition accuracy of about 92.8% from 12000 data

A System for Bangla Handwritten Numeral Recognition

Colloque avec actes et comité de lecture. internationale.International audienceThis paper deals with a recognition system for unconstrained off-line Bangla handwritten numerals. To take care of variability involved in the writing style of different individuals, a robust scheme is presented here. The scheme is mainly based on new features obtained from the concept of water overflow from the reservoir as well as topological and structural features of the numerals. The proposed scheme is tested on data collected from different individuals of various background and we obtained an overall recognition accuracy of about 92.8% from 12000 data

A System for Bangla Handwritten Numeral Recognition

Colloque avec actes et comité de lecture. internationale.International audienceThis paper deals with a recognition system for unconstrained off-line Bangla handwritten numerals. To take care of variability involved in the writing style of different individuals, a robust scheme is presented here. The scheme is mainly based on new features obtained from the concept of water overflow from the reservoir as well as topological and structural features of the numerals. The proposed scheme is tested on data collected from different individuals of various background and we obtained an overall recognition accuracy of about 92.8% from 12000 data

Accuracy Affecting Factors for Optical Handwritten Character Recognition

Optiline kirjatuvastus viitab tehnikale, mis konverteerib trükitud, kirjutatud või prinditud teksi masinkodeeritud tekstiks, võimaldades sellega paberdokumentide nagu passide, arvete, meditsiiniliste vormide või tšekkide automaatset töötlemist. Mustrituvastus, tehisintellekt ja arvuti nägemine on kõik teadusharud, mis võimaldavad optilist kirjatuvastust. Optilise kirjatuvastuse kasutus võimaldaks paljudel kasvavatel informatsiooni süsteemidel mugavat üleminekut paberformaadilt digitaalsele. Tänapäeval on optilisest kirjatuvastusest väljaskasvanud mitme sammuline protsess: segmenteerimine, andmete eeltöötlus, iseloomulike tunnuste tuletamine, klassifitseerimine, andmete järeltöötlus ja rakenduse spetsiifiline optimiseerimine. See lõputöö pakub välja tehnikaid, millega üleüldiselt tõsta optiliste kirjatuvastussüsteemide täpsust, näidates eeltöötluse, iseloomulike tunnuste tuletamise ja morfoloogilise töötluse mõju. Lisaks võrreldakse erinevate enimkasutatud klassifitseerijate tulemusi. Kasutades selles töös mainitud meetodeid saavutati täpsus üle 98% ja koguti märkimisväärselt suur andmebaas käsitsi kirjutatud jaapani keele hiragana tähestiku tähti.Optical character recognition (OCR) refers to a technique that converts images of typed, handwritten or printed text into machine-encoded text enabling automatic processing paper records such as passports, invoices, medical forms, receipts, etc. Pattern recognition, artificial intelligence and computer vision are all research fields that enable OCR. Using OCR on handwritten text could greatly benefit many of the emerging information systems by ensuring smooth transition from paper format to digital world. Nowadays, OCR has evolved into a multi-step process: segmentation, pre-processing, feature extraction, classification, post-processing and application-specific optimization. This thesis proposes techniques to improve the overall accuracy of the OCR systems by showing the affects of pre-processing, feature extraction and morphological processing. It also compares accuracies of different well-known and commonly used classifiers in the field. Using the proposed techniques an accuracy of over 98% was achieved. Also a dataset of handwritten Japanese Hiragana characters with a considerable variability was collected as a part of this thesis

A Novel Two-Stage Spectrum-Based Approach for Dimensionality Reduction: A Case Study on the Recognition of Handwritten Numerals

Dimensionality reduction (feature selection) is an important step in pattern recognition systems. Although there are different conventional approaches for feature selection, such as Principal Component Analysis, Random Projection, and Linear Discriminant Analysis, selecting optimal, effective, and robust features is usually a difficult task. In this paper, a new two-stage approach for dimensionality reduction is proposed. This method is based on one-dimensional and two-dimensional spectrum diagrams of standard deviation and minimum to maximum distributions for initial feature vector elements. The proposed algorithm is validated in an OCR application, by using two big standard benchmark handwritten OCR datasets, MNIST and Hoda. In the beginning, a 133-element feature vector was selected from the most used features, proposed in the literature. Finally, the size of initial feature vector was reduced from 100% to 59.40% (79 elements) for the MNIST dataset, and to 43.61% (58 elements) for the Hoda dataset, in order. Meanwhile, the accuracies of OCR systems are enhanced 2.95% for the MNIST dataset, and 4.71% for the Hoda dataset. The achieved results show an improvement in the precision of the system in comparison to the rival approaches, Principal Component Analysis and Random Projection. The proposed technique can also be useful for generating decision rules in a pattern recognition system using rule-based classifiers

Fourier descriptors and handwritten digit recognition

This paper presents the results of a comparative study of various Fourier descriptor representations and their use in recognition of unconstrained handwritten digits. Certain characteristics of five distinct Fourier descriptor representations of handwritten digits are discussed, and illustrations of ambiguous digit classes introduced by use of these Fourier descriptor representations are presented. It is concluded that Fourier descriptors are practically effective only within the framework of an intelligent system, capable of reasoning about digit hypotheses. We describe a hypothesisgenerating algorithm based on Fourier descriptors which allows a classifier to associate more than one digit class with each input. Such hypothesis-generating schemes can be very effective in systems employing multiple classifiers. We compare the performance of the five Fourier descriptor representations based on experiment results produced by a particular hypothesis-generating classifier for a test set of 14000 handwritten digits. It is found that some Fourier descriptor formulations are more successful than others for handwritten digit recognition.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46057/1/138_2005_Article_BF01212429.pd

GEMA: An open-source Python library for self-organizing-maps

Organizations have realized the importance of data analysis and its benefits. This in combination with Machine Learning algorithms has allowed to solve problems more easily, making these processes less time-consuming. Neural networks are the Machine Learning technique that is recently obtaining very good best results. This paper describes an open-source Python library called GEMA developed to work with a type of neural network model called Self-Organizing-Maps. GEMA is freely available under GNU General Public License at GitHub (https://github.com/ufvceiec/GEMA). The library has been evaluated in different a particular use case obtaining accurate results

Visual pattern recognition using neural networks

Neural networks have been widely studied in a number of fields, such as neural architectures, neurobiology, statistics of neural network and pattern classification. In the field of pattern classification, neural network models are applied on numerous applications, for instance, character recognition, speech recognition, and object recognition. Among these, character recognition is commonly used to illustrate the feature and classification characteristics of neural networks. In this dissertation, the theoretical foundations of artificial neural networks are first reviewed and existing neural models are studied. The Adaptive Resonance Theory (ART) model is improved to achieve more reasonable classification results. Experiments in applying the improved model to image enhancement and printed character recognition are discussed and analyzed. We also study the theoretical foundation of Neocognitron in terms of feature extraction, convergence in training, and shift invariance. We investigate the use of multilayered perceptrons with recurrent connections as the general purpose modules for image operations in parallel architectures. The networks are trained to carry out classification rules in image transformation. The training patterns can be derived from user-defmed transformations or from loading the pair of a sample image and its target image when the prior knowledge of transformations is unknown. Applications of our model include image smoothing, enhancement, edge detection, noise removal, morphological operations, image filtering, etc. With a number of stages stacked up together we are able to apply a series of operations on the image. That is, by providing various sets of training patterns the system can adapt itself to the concatenated transformation. We also discuss and experiment in applying existing neural models, such as multilayered perceptron, to realize morphological operations and other commonly used imaging operations. Some new neural architectures and training algorithms for the implementation of morphological operations are designed and analyzed. The algorithms are proven correct and efficient. The proposed morphological neural architectures are applied to construct the feature extraction module of a personal handwritten character recognition system. The system was trained and tested with scanned image of handwritten characters. The feasibility and efficiency are discussed along with the experimental results

Preserving known anatomical topology in medical image segmentation using deep learning

Since the rise of deep learning, new medical image segmentation methods have rapidly been proposed with promising results, with each one reporting marginal improvements on the previous state-of-the-art (SOTA) method. However, on visual inspection, errors are often revealed, such as topological mistakes (e.g. holes or folds), that are not detected using traditional evaluation metrics, such as Dice. Moreover, correct topology is often essential in ensuring segmentations are anatomically and pathologically plausible and, ultimately, suitable for downstream image processing tasks. Therefore, there is a need for methods to focus on ensuring that the predicted segmentations are topologically correct, rather than just optimising the pixel-wise accuracy. In this thesis, I propose a method that utilises prior knowledge of anatomy to segment structures, whilst preserving the known topology. The presented model, Topology Encouraging Deformation Segmentation Network (TEDS-Net), performs segmentation by deforming a prior shape with the same topological features as the anatomy of interest using learnt topology-preserving deformation fields. However, here I show that such fields only guarantee topology preservation in the continuous domain and that their properties begin to break down when applied in discrete spaces. To overcome this effect, I introduced additional modifications in TEDS-Net to more strictly enforce topology preservation, a step often overlooked in previous work. Across this thesis, TEDS-Net is applied to a range of natural and medical image segmentation tasks. I show how it can be used for multiple topology types, multiple structures and in both two- and three-dimensions. Further, I show how TEDS-Net can be used to segment whole volumes using minimally annotated training data. Across these experiments, TEDS-Net outperforms all SOTA baselines on topology, whilst maintaining competitive pixel-wise accuracy. Finally, TEDS-Net is integrated into a whole medical imaging pipeline, to illustrate the importance of topologically correct segmentations for downstream tasks. TEDS-Net is used to segment the developing cortical plate from in-utero fetal brain ultrasound scans in 3D, to enable the characterisation of its complex growth and development during gestation. To the best of my knowledge, this task has only been previously attempted from magnetic resonance imaging (MRI), despite ultrasound being the modality of choice in prenatal care. This is likely due to large acoustic shadows obstructing key brain regions in ultrasound. Due to TEDS-Net anatomical constraints, it can anatomically guide the cortical plate segmentation in regions of shadows, producing a complete segmentation that enables accurate downstream analysis