An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings

An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model. The main contribution of this paper is to avoid heuristic-based methods for string preprocessing and segmentation, the need for task-oriented classifiers, and also the use of specific constraints related to the string length. A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition. Besides, it reduces the complexity of the string recognition task considerably since it drops out classical steps, in special preprocessing, segmentation, and a set of classifiers devoted to strings with a specific length

Off-line Thai handwriting recognition in legal amount

Thai handwriting in legal amounts is a challenging problem and a new field in the area of handwriting recognition research. The focus of this thesis is to implement Thai handwriting recognition system. A preliminary data set of Thai handwriting in legal amounts is designed. The samples in the data set are characters and words of the Thai legal amounts and a set of legal amounts phrases collected from a number of native Thai volunteers. At the preprocessing and recognition process, techniques are introduced to improve the characters recognition rates. The characters are divided into two smaller subgroups by their writing levels named body and high groups. The recognition rates of both groups are increased based on their distinguished features. The writing level separation algorithms are implemented using the size and position of characters. Empirical experiments are set to test the best combination of the feature to increase the recognition rates. Traditional recognition systems are modified to give the accumulative top-3 ranked answers to cover the possible character classes. At the postprocessing process level, the lexicon matching algorithms are implemented to match the ranked characters with the legal amount words. These matched words are joined together to form possible choices of amounts. These amounts will have their syntax checked in the last stage. Several syntax violations are caused by consequence faulty character segmentation and recognition resulting from connecting or broken characters. The anomaly in handwriting caused by these characters are mainly detected by their size and shape. During the recovery process, the possible word boundary patterns can be pre-defined and used to segment the hypothesis words. These words are identified by the word recognition and the results are joined with previously matched words to form the full amounts and checked by the syntax rules again. From 154 amounts written by 10 writers, the rejection rate is 14.9 percent with the recovery processes. The recognition rate for the accepted amount is 100 percent

The Anatomy of Bangla OCR System for Printed Texts using Back Propagation Neural Network

This paper is based on Bangla (National Language of Bangladesh) Optical Character Recognition process for printed texts and its steps using Back Propagation Neural Network. Bangla character recognition is very important field of research because Bangla is most popular language in the Indian subcontinent. Pre-processing steps that follows are Image Acquisition, binarization, background removal, noise elimination, skew angle detection and correction, noise removal, line, word and character segmentations. In the post processing steps various features are extracted by applying DCT (Discrete Cosine Transform) from segmented characters. The segmented characters are then fed into a three layer feed forward Back Propagation Neural Network for training. Finally this network is used to recognize printed Bangla scripts

Integration of traditional imaging, expert systems, and neural network techniques for enhanced recognition of handwritten information

Includes bibliographical references (p. 33-37).Research supported by the I.F.S.R.C. at M.I.T.Amar Gupta, John Riordan, Evelyn Roman

Recognition of off-line arabic handwritten dates and numeral strings

In this thesis, we present an automatic recognition system for CENPARMI off-line Arabic handwritten dates collected from Arabic Nationalities. This system consists of modules that segment and recognize an Arabic handwritten date image. First, in the segmentation module, the system explicitly segments a date image into a sequence of basic constituents or segments. As a part of this module, a special sub-module was developed to over-segment any constituent that is a candidate for a touching pair. The proposed touching pair segmentation submodule has been tested on three different datasets of handwritten numeral touching pairs: The CENPARMI Arabic [6], Urdu, and Dari [24] datasets. The final recognition rates of 92.22%, 90.43%, and 86.10% were achieved for Arabic, Urdu and Dari, respectively. Afterwards, the segments are preprocessed and sent to the classification module. In this stage, feature vectors are extracted and then recognized by an isolated numeral classifier. This recognition system has been tested in five different isolated numeral databases: The CENPARMI Arabic [6], Urdu, Dari [24], Farsi, and Pashto databases with overall recognition rates of 97.29% 97.75%, 97.75%, 97.95% and 98.36%, respectively. Finally, a date post processing module is developed to improve the recognition results. This post processing module is used in two different stages. First, in the date stage, to verify that the segmentation/recognition output represents a valid date image and it chooses the best date format to be assigned to this image. Second, in the sub-field stage, to evaluate the values for the date three parts: day, month and year. Experiments on two different databases of Arabic handwritten dates: CENPARMI Arabic database [6] and the CENPARMI Arabic Bank Cheques database [7], show encouraging results with overall recognition rates of 85.05% and 66.49, respectively

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

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