Artificial Intelligence Recognition of Children's Handwritten English Letters: Accuracy, Error Patterns, and Educational Applications

Recognition of standard handwriting is efficient, but children's English letters are challenging because their shapes are under development. They have different directions, sizes, spacing, positioning, and orientations of strokes. All this makes the subtle differences between similar letters unnoticeable for recognition algorithms. This review covers the accuracy of recognition, frequent mistakes, and educational applications. Numerous research show impressive results on children's handwriting recognition in controlled conditions. However, recognition depends on case, regularity, quality of images, and the way of testing. The main problems are similar letters such as b-d, p-q, u-v, and i-j-I-l. Small mostly privately collected child datasets also reduce the possibility of comparison and application in classrooms. Recognizing algorithms could be used to detect attempts of practicing, provide immediate feedback on formation and measure the development of children. To use them safely, there must be confidence thresholds, teacher reviews, user-friendly interfaces and strict data protection rules. Now, these systems are good for controlled practice but not for assessment or diagnosis.