A SVM-based cursive character recognizer

Abstract This paper presents a cursive character recognizer, a crucial module in any cursive word recognition system based on a segmentation and recognition approach. The character classification is achieved by using support vector machines (SVMs) and a neural gas. The neural gas is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, the character recognition is performed by SVMs. A database of 57 293 characters was used to train and test the cursive character recognizer. SVMs compare notably better, in terms of recognition rates, with popular neural classifiers, such as learning vector quantization and multi-layer-perceptron. SVM recognition rate is among the highest presented in the literature for cursive character recognition

Use of colour for hand-filled form analysis and recognition

Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system

Handwritten character recognition using a gradient based feature extraction

Handwriting Recognition is the task of transforming a language that is represented in its spatial form of graphical marks into its symbolic representation. In Offline Handwriting Recognition, there are three steps: preprocessing of the image, segmentation of words into characters and recognition of the characters. In this thesis I implemented two methods for character recognition, which is the most important step in Offline Handwriting Recognition. The heart of character recognition is the features that are extracted from the character image. The accuracy of the classification of the character image depends on the quality of the features extracted from the image. The two methods presented in this thesis use two different types of features. One uses the connectivity features among various segments in a character image, and the other method uses the gradient feature at each pixel to construct the feature vectors. Both these methods are discussed in detail in the following chapters

PENGENALAN KARAKTER HURUF HIJAIYAH MENGGUNAKAN METODE MODIFIED DIRECTION FEATURE (MDF) DAN METODE LEARNING VECTOR QUANTIZATION 3 (LVQ 3)

Salah satu huruf yang memiliki karakteristik unik adalah huruf Hijaiyah. Karakteristik dari huruf Hijaiyah dapat berubah berdasarkan peletakkan huruf tunggal di awal, tengah dan akhir kata. Cara lain untuk mengenali karakter huruf Hijaiyah selain dengan memperhatikan karakteristik masing-masing huruf adalah dengan memanfaatkan pengenalan pola dan jaringan syaraf tiruan. Dalam penelitian ini, proses ektraksi ciri yang digunakan adalah metode Modified Direction Feature (MDF) dan proses klasifikasi yang digunakan metode Learning Vector Quantization 3 (LVQ 3). Pengujian yang dilakukan yaitu: pengujian ukuran matriks citra yang terdiri dari 80x80 piksel, 100x100 piksel, dan 120x120 piksel; dan pegujian nilailearning rate 0.01, 0.03,0.05, dan 0.07. Dari hasil pengujian yang dilakukan adalah sistem mampu mengenali pola karakter Huruf Hijaiyah dengan akurasi terbaik adalah 82.44% pada matriks citra berukuran 120x120 piksel dengan learning rate 0.03. Kata Kunci: Huruf Hijaiyah, Pengenalan Pola, Modofied Direction Feature, Learning Vector Quantizatio

Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images

There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio

Character Queries: A Transformer-based Approach to On-Line Handwritten Character Segmentation

On-line handwritten character segmentation is often associated with handwriting recognition and even though recognition models include mechanisms to locate relevant positions during the recognition process, it is typically insufficient to produce a precise segmentation. Decoupling the segmentation from the recognition unlocks the potential to further utilize the result of the recognition. We specifically focus on the scenario where the transcription is known beforehand, in which case the character segmentation becomes an assignment problem between sampling points of the stylus trajectory and characters in the text. Inspired by the $k$-means clustering algorithm, we view it from the perspective of cluster assignment and present a Transformer-based architecture where each cluster is formed based on a learned character query in the Transformer decoder block. In order to assess the quality of our approach, we create character segmentation ground truths for two popular on-line handwriting datasets, IAM-OnDB and HANDS-VNOnDB, and evaluate multiple methods on them, demonstrating that our approach achieves the overall best results.Comment: ICDAR 2023 Best Student Paper Award. Code available at https://github.com/jungomi/character-querie