Ai driven ocr: Resolving handwritten fonts recognizability problems

Abstract

Optical Character Recognition (OCR) is the electronic or mechanical conversion of images of typed, handwritten, or printed text into machine-encoded text. Advanced systems are capable to produce a high degree of recognition accuracy for most technic fonts, but when it comes to handwritten forms there is a problem occur in recognizing certain characters and limitations with conventional OCR processes persist. It is most pronounced in ascenders (k, b, l, d, h, t) and descenders (g, j, p, q, y). If the characters are linked by ligatures, the ascending and descending strokes are even less recognizable to the scanners. In order to reduce the likelihood of a recognition error, it is a necessary to create a large database of stored characters and their glyphs. Feature extraction decomposes glyphs into features like lines, closed loops, line direction, and line intersections. A Multilayer Perceptron (MLP) neural network based on Back Propagation Neural Network (BPNN) algorithm as a method of Artificial Intelligence (AI) has been used in text identification, classification and recognition using various methods: image pattern based, text-based, mark-based etc. Also, the application of AI generates of a large database of different letter cuts, and modifications, and variation of the same letter character structure. For this purpose, the recognizability test of handwritten fonts was performed. Within main group, subgroups of independent letter characters and letter characters linked by ligatures are created, and reading errors were observed. In each subgroup, four different font families (bold stroke, alternating stroke, monoline stroke, and brush stroke) were tested. In subgroup of independent letter characters, errors were observed in similar rounded lines such as the characters a, and e. In the subgroup of letter characters linked by ligatures, errors were also observed in similar rounded lines such as the letter characters a and e, m and n, but also in ascenders b and l, and descenders g and q. Furthermore, seven letter cuts were made from each basic test letters, and up to are thin, ultra-light, light, regular, semi-bold, bold, and ultra-bold, and stored in the existing EMNIST database. The scanning test was repeated, and recently obtained results showed a decrease in the deviation rate, i.e. higher accuracy. Reducing the number of deviations shows that the neural network gives acceptable answers but requires creation of a larger database within about 56,000 different characters.