Handwritten text recognition is one of the most valuable recognition systems because of the unique characteristics of each person's handwriting. Thus, recognition systems need to be more adaptable to recognize same or different characters with different characteristics. On the other hand, one of the most challenging tasks in handwritten text recognition problems is recognizing ancient documents which include several noise within them. While digitizing these documents, these noise appear in different types which effects any recognition system. So, digitizing ancient documents, applying proper pre-processing techniques and performing effective classifier are the main steps of efficient recognition system. In this paper, a complete Ethiopian ancient Geez character recognition system using deep convolutional neural network is proposed in order to recognize twenty-six base characters of this alphabet. The proposed system obtained an accuracy of 99.39% with a model loss of 0.044 which demonstrates its efficiency.

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Ancient Geez script recognition using deep learning

There exist huge amount of valuable historical Devanagari documents archived in many national libraries that need to be preserved in digital form. Additionally, there is a growing requirement in the area of document image processing for automation and information extraction from old handwritten documents. Retrieval of relevant information from handwritten historical document images would ideally necessitate an efficient An alternate approach to accurate document transcription is to use a keyword spotting method. The keyword spotting system's primary application is to create digital libraries that facilitate quick searching and browsing of old manuscripts in order to preserve the world's cultural heritage. For word spotting in handwritten manuscripts, we propose a convolution neural network (CNN) architecture based on Autoencoder representation. The proposed technique was demonstrated using historical handwritten Devanagari manuscript which is collected from Oriental Research Institute of University of Mysore, Mysore. As a result, the proposed convolution neural network (CNN) method exhibits superior accuracy with favorable results. 

A Convolutional Autoencoder based Keyword Spotting in Historical Handwritten Devanagari Documents

India is a country of various languages right from Kashmir to Kanyakumari. The national language of India is Hindi which is also the third most popular language in the world. The script in which the Hindi language is written is known as Devanagari script which in fact is used to write many other languages such as Sanskrit, Marathi, Nepali, and Konkani languages. Neural networks are recently being used in several different ways of pattern identification. It is common knowledge that every person’s handwriting is dissimilar. Therefore, it is challenging to recognize those handwritten monograms. The sector of pattern recognition that has become a hot topic for research purposes is handwritten character recognition. This is where neural networks play an important role. The competence of a computer to take in and decipher comprehensible transcribed input whose origin is paper documents, touch screens, photographs, and alternative gadgets are termed as handwriting recognition. Handwritten recognition of words is a model which is used to convert the written text into words that are crucial in the human computer interface. The handwriting recognition area is an extensively experimented branch till date and the Devanagari script recognition is progressing area of research. The above application is used in mail sorting, office automation, cheque verification, and human computer communication, i.e. the growing age of artificial intelligence. A sample of the dataset of images which are centralized and grayscale are considered and analysed using the K-nearest neighbour classification, extremely randomized decision forest classification and random forest classification are considered. 

Hand Written Devanagari Script Short Scale Character Recognition

In India, many people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. Basically Character recognition techniques associate a symbolic identity with the image of a character. Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with the program. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date.

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Aradhana A Malanker

Papers

Handwritten Devanagari Script Recognition: A Survey