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We describe Google's online handwriting recognition system that currently supports 22 scripts and 97 languages. The system's focus is on fast, high-accuracy text entry for mobile, touch-enabled devices. We use a combination of state-of-the-art components and combine them with novel additions in a flexible framework. This architecture allows us to easily transfer improvements between languages and...
We propose a model of feature selection for offline handwriting recognition. The targeted area is recognition of Khmer handwritten text. We make use of correlation of features, two dimensional Fourier transformation and Gabor filters. We also pass the reduced data through a distance-based classifier to compare performance of each method. Feature selection is an important step towards improving recognition...
This paper proposes a model for an offline handwritten Khmer character recognition. We make use of two dimensional Fourier transformation for feature selection and feed-forward Artificial Neural Net as classification tool. The recognition system allows using the nature of Khmer writing, which is an example of alphasyllabary (Abugida) writing systems. The recognition of the normalized handwritten images...
Modi was very useful script in the kingdoms of medieval Maharashtra. In the reign of the great Maratha-Chhatrapati Shivaji and also in the reign of Peshwas, this script was widely incorporated in ruling the state. This script is very similar to the shorthand. At that time, it was used in Maharashtra to prepare the documents such as Property matters, Donation of Land (Dan-Patra), Land Revenue, Military...
A new online handwritten Mongolian word database, MRG-OHMW, is introduced in this paper. This database contains 946 Mongolian words produced by 300 persons from Mongolian ethnic minority. These Mongolian words are composed of one to fourteen Mongolian characters, and selected from large-scale Mongolian text corpus according to the frequencies of usage. The current version of this database is collected...
In this paper, a study is conducted on combining analytical and holistic strategies for handwriting recognition. Even though the big majority of the recent high recognition rate systems adopts analytical strategies, physiological scientists suggest that the holistic strategy is the key for realizing near-human performance. In what we believe is a fresh perspective on handwriting recognition, combining...
We propose a novel approach for helping content transcription of handwritten digital documents. The approach adopts a segmentation based keyword retrieval approach that follows query-by-string paradigm and exploits the user validation of the retrieved words to improve its performance during operation. Our approach starts with an initial training set, which contains only a few pages and a tentative...
This competition is aimed at classification of writer demographics from offline handwritten documents using the QUWI database. QUWI is a bilingual database comprising writing samples of same individuals in Arabic and English. This allows evaluating the performance of different systems in a more challenging multi-script environment. This paper presents the details of the competition tasks, the datasets...
Text-independent writer identification is challenging due to the huge variation of written contents and the ambiguous written styles of different writers. This paper proposes DeepWriter, a deep multi-stream CNN to learn deep powerful representation for recognizing writers. DeepWriter takes local handwritten patches as input and is trained with softmax classification loss. The main contributions are:...
Handwritten character recognition has been one of the most fascinating research among the various researches in field of image processing. In Handwritten character recognition method the input is scanned from images, documents and real time devices like tablets, tabloids, digitizers etc. which are then interpreted into digital text. There are basically two approaches — Online Handwritten recognition...
With the invention of Microsoft Kinect sensor, human-computer interaction is gaining its attention and becoming available for widespread use. The previous study presented a method of Kinect-based mid-air handwritten digit recognition for Android smart phones with a recognition accuracy of only about 94.6%. In this paper, we propose an improved method based on the normalizing and scaling of path order...
This paper presents a high-performance two-stage cascade CNN model. The main idea behind the cascade CNN model is complementary classification objectives between Stage I and Stage II. Discriminative learning is introduced to train Stage II by feeding back poorly recognized training samples. Experiments have been conducted on the competitive MNIST handwritten digit database. The cascade model achieved...
Identification of individuals from handwritten documents using automated recognition systems has gained significant research interest due to the wide variety of applications it offers for forensic analysis, signature verification, classification of historical writings and other document analysis tasks. In this paper, we present a framework that combines different feature space representations of handwriting...
Hidden Markov Models (HMM) are the widely used modeling techniques for online handwriting recognition. This paper describes both stroke based and character based methods for Assamese handwritten character recognition using HMM classifier. In stroke based method, unique strokes that are used to write the characters are grouped and then HMM modeling is done for each of these selected class of strokes...
The writer adaptation arisen with the appearance and the excessive use of Handheld devices. These devices are conceived to be used in diverse user settings which can be stationary or mobile. Most of the works tackle the writer adaptation in the "sitting at a desk" environment, nevertheless we notice a lack of contributions in the multi-environment context. In this paper we present a multi-environment...
Restricted Boltzmann machines (RBMs) and their variants have attracted a lot of attention recently. They have been applied widely, e.g., In handwriting recognition, document categorization and object recognition. Unfortunately, an RBM requires a large parameter space since it is a fully-connected bipartite graph, especially with high dimensional input spaces. Moreover, it is still unclear how it selects...
Writer identification is the process of determining the author of a handwritten specimen by utilizing characteristics inherent in the sample. In this work, we apply the concept of accents in handwriting to introduce a novel perspective for writer identification. Analogous to speech, accents in handwriting can be defined as distinctive writing quirks that are unique to a group of people sharing a common...
Codebook-based representations have been effectively employed for writer identification. Most of the codebook-based methods generate a codebook by clustering a set of patterns extracted from an independent data set. The probability of occurrence of the codebook patterns in a given writing is then used to characterize its author. This study investigates the hypothesis that the codebook is merely a...
Accent in speech is defined as a distinctive mode of pronunciation that is unique to a geographical region. In a similar way, we define accent in handwriting as distinctive writing characteristics that are unique to a group of people sharing a common native script. In other words, we postulate that a group of people with a common native script will share certain traits in their handwriting that can...
In overlaid handwriting, multiple characters are written sequentially in the same area. This needs special consideration for segmenting the stroke sequence into characters. We propose a learning-based model for scoring the candidate stroke cuts and segments for online overlaid Chinese handwriting recognition. Based on stroke cut classification using support vector machine (SVM), strokes are grouped...
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