Today is the era of paperless office and governance. It comes with numerous advantages like increased productivity and efficiency, pervasiveness, storage optimization, robustness and eco-friendliness. Hence there is a need of converting paper documents into machine editable form. This leads to development of OCR (Optical Character Recognition). OCR is a technique to convert, mechanically or electronically an image, photo or scanned document of a handwritten text (HCR-Handwritten Character Recognition) or printed text (PCR- Printed Character Recognition) into digital text. HCR is a form of OCR that is specifically designed to recognize the handwritten text whereas PCR focuses on recognition of printed text. HCR is more challenging as compared to PCR due to diversity in human writing styles, size, curve, strokes and thickness of characters. Based on data acquisition mode, the OCR can either be online or offline. Offline recognition is performed in two ways: handwritten and printed [1]. In offline mode, the characters are on paper and captured using scanner or high-resolution camera whereas in online mode the pixel values of characters are captured by movement of cursor, pen or stylus. The HCR systems are readily available for foreign languages and many of the Indian languages like Bangla, Devanagari and Gurumukhi but for Gujarati language the HCR development is still in its infancy stage. This study focuses on development of an artificial intelligence based offline HCR system for Gujarati language. Important contribution of this study is data collection, of size 10,000 images from 250 number of people, of different age groups, of different professions. This paper describes a supervised classifier approach based on CNN (Convolutional Neural Networks) and MLP (Multi-Layer Perceptron) for recognition of handwritten Gujarati characters. A success rate of 97.21% is obtained using CNN and 64.48% using MLP. Lot of work has been done at character level, but very few has been done at word level recognition. Major focus of this study was on creating a continuous workflow for image to text conversion at word level.
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