Open Set Chinese Character Recognition using Multi-typed Attributes

Recognition of Off-line Chinese characters is still a challenging problem, especially in historical documents, not only in the number of classes extremely large in comparison to contemporary image retrieval methods, but also new unseen classes can be expected under open learning conditions (even for CNN). Chinese character recognition with zero or a few training samples is a difficult problem and has not been studied yet. In this paper, we propose a new Chinese character recognition method by multi-type attributes, which are based on pronunciation, structure and radicals of Chinese characters, applied to character recognition in historical books. This intermediate attribute code has a strong advantage over the common `one-hot' class representation because it allows for understanding complex and unseen patterns symbolically using attributes. First, each character is represented by four groups of attribute types to cover a wide range of character possibilities: Pinyin label, layout structure, number of strokes, three different input methods such as Cangjie, Zhengma and Wubi, as well as a four-corner encoding method. A convolutional neural network (CNN) is trained to learn these attributes. Subsequently, characters can be easily recognized by these attributes using a distance metric and a complete lexicon that is encoded in attribute space. We evaluate the proposed method on two open data sets: printed Chinese character recognition for zero-shot learning, historical characters for few-shot learning and a closed set: handwritten Chinese characters. Experimental results show a good general classification of seen classes but also a very promising generalization ability to unseen characters.Comment: 29 pages, submitted to Pattern Recognitio

Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead

Deep Learning Based Real Time Devanagari Character Recognition

The revolutionization of the technology behind optical character recognition (OCR) has helped it to become one of those technologies that have found plenty of uses in the entire industrial space. Today, the OCR is available for several languages and have the capability to recognize the characters in real time, but there are some languages for which this technology has not developed much. All these advancements have been possible because of the introduction of concepts like artificial intelligence and deep learning. Deep Neural Networks have proven to be the best choice when it comes to a task involving recognition. There are many algorithms and models that can be used for this purpose. This project tries to implement and optimize a deep learning-based model which will be able to recognize Devanagari script’s characters in real time by analyzing the hand movements

Towards robust real-world historical handwriting recognition

In this thesis, we make a bridge from the past to the future by using artificial-intelligence methods for text recognition in a historical Dutch collection of the Natuurkundige Commissie that explored Indonesia (1820-1850). In spite of the successes of systems like 'ChatGPT', reading historical handwriting is still quite challenging for AI. Whereas GPT-like methods work on digital texts, historical manuscripts are only available as an extremely diverse collections of (pixel) images. Despite the great results, current DL methods are very data greedy, time consuming, heavily dependent on the human expert from the humanities for labeling and require machine-learning experts for designing the models. Ideally, the use of deep learning methods should require minimal human effort, have an algorithm observe the evolution of the training process, and avoid inefficient use of the already sparse amount of labeled data. We present several approaches towards dealing with these problems, aiming to improve the robustness of current methods and to improve the autonomy in training. We applied our novel word and line text recognition approaches on nine data sets differing in time period, language, and difficulty: three locally collected historical Latin-based data sets from Naturalis, Leiden; four public Latin-based benchmark data sets for comparability with other approaches; and two Arabic data sets. Using ensemble voting of just five neural networks, a level of accuracy was achieved which required hundreds of neural networks in earlier studies. Moreover, we increased the speed of evaluation of each training epoch without the need of labeled data

Novel word recognition and word spotting systems for offline Urdu handwriting

Word recognition for offline Arabic, Farsi and Urdu handwriting is a subject which has attained much attention in the OCR field. This thesis presents the implementations of offline Urdu Handwritten Word Recognition (HWR) and an Urdu word spotting technique. This thesis first introduces the creation of several offline CENPARMI Urdu databases. These databases were necessary for offline Urdu HWR experiments. The holistic-based recognition approach was followed for the Urdu HWR system. In this system, the basic pre-processing of images was performed. In the feature extraction phase, the gradient and structural features were extracted from greyscale and binary word images, respectively. This recognition system extracted 592 feature sets and these features helped in improving the recognition results. The system was trained and tested on 57 words. Overall, we achieved a 97 % accuracy rate for handwritten word recognition by using the SVM classifier. Our word spotting technique used the holistic HWR system for recognition purposes. This word spotting system consisted of two processes: the segmentation of handwritten connected components and diacritics from Urdu text lines and the word spotting algorithm. A small database of handwritten text pages was created for testing the word spotting system. This database consisted of texts from ten Urdu native speakers. The rule-based segmentation system was applied for segmentation (or extracting) for handwritten Urdu subwords or connected components from text lines. We achieved a 92% correct segmentation rate for 372 text lines. In the word spotting algorithm, the candidate words were generated from the segmented connected components. These candidate words were sent to the holistic HWR system, which extracted the features and tried to recognize each image as one of the 57 words. After classification, each image was sent to the verification/rejection phase, which helped in rejecting the maximum number of unseen (raw data) images. Overall, we achieved a 50% word spotting precision at a 70% recall rat

Subword Recognition in Historical Arabic Documents using C-GRUs

The recent years have witnessed an increased tendency to digitize historical manuscripts that not only ensures the preservation of these collections but also allows researchers and end-users’ direct access to these images. Recognition of Arabic handwriting is challenging due to the highly cursive nature of the script and other challenges associated with historical documents (degradation etc.). This paper presents an end-to-end system to recognize Arabic handwritten sub words in historical documents. More specifically, we introduce a hybrid CNN-GRU model where the shallow convolutional network learns robust feature representations while the GRU layers carry out the sequence modelling and generate the transcription of the text. The proposed system is evaluated on two different datasets, IBN SINA and VML-HD reporting recognition rates of 96.10% and 98.60% respectively. A comparison with existing techniques evaluated on the same datasets validates the effectiveness of our proposed model in characterizing Arabic subwords

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches